CDK4/6 and CH
Irenaeus Chan
Thu May 29, 2025 14:56:40
knitr::opts_chunk$set(dev = "svg")
library(tidyverse)
library(data.table)
library(table1)
library(ggpubr)
library(ggthemr)
library(ggsci)
library(cowplot)
library(ggplot2)
library(geepack)
library(sjPlot)
library(readxl)
source("/Users/irenaeuschan/Documents/Irenaeus/CDK46_CH/utils/toolbox.R")
source("/Users/irenaeuschan/Documents/Irenaeus/CDK46_CH/utils/timepoints_function.R")
source("/Users/irenaeuschan/Documents/Irenaeus/CDK46_CH/utils/custom_lollipop.R")
data_file_path <- "/Users/irenaeuschan/Documents/Irenaeus/CDK46_CH/data/"
figures_save_path <- "/Users/irenaeuschan/Documents/Irenaeus/CDK46_CH/figures/"# CH Calls
sclc_df <- fread(paste0(data_file_path, "g1_sclc_df.minimum.csv")) # Small Cell Lung Cancer
tnbc_df <- fread(paste0(data_file_path, "g1_tnbc_df.minimum.csv")) # Triple Negative Breast Cancer
crc_df <- fread(paste0(data_file_path, "g1_crc_df.minimum.csv")) # Colorectal Cancer
untreated_df <- fread(paste0(data_file_path, "untreated_df.minimum.csv")) # Untreated
g1_df <- rbind(
sclc_df %>% mutate(Cohort = "SCLC") %>% mutate(DeID = as.character(DeID)),
tnbc_df %>% mutate(Cohort = "TNBC") %>% mutate(DeID = as.character(DeID), SampleDeID = as.character(SampleDeID)),
crc_df %>% mutate(Cohort = "CRC"),
untreated_df %>% mutate(Cohort = "Untreated") %>% mutate(SampleDeID = as.character(SampleDeID))
)
sclc_schematic <- paste0(data_file_path, "G1_SCLC.png")
tnbc_schematic <- paste0(data_file_path, "G1_TNBC.png")
crc_schematic <- paste0(data_file_path, "G1_CRC.png")
# Timepoints Dataframe
sclc_tp <- fread(paste0(data_file_path, "sclc_timepoints.minimum.csv")) # Small Cell Lung Cancer
tnbc_tp <- fread(paste0(data_file_path, "tnbc_timepoints.minimum.csv")) # Triple Negative Breast Cancer
crc_tp <- fread(paste0(data_file_path, "crc_timepoints.minimum.csv")) # Colorectal Cancer
g1_tp <- rbind(
sclc_tp %>% mutate(Cohort = if_else(Trilaciclib == "Untreated", "Untreated", "SCLC")),
tnbc_tp %>% mutate(Cohort = "TNBC"),
crc_tp %>% mutate(Cohort = "CRC")
) %>%
mutate(
prepreVAF = ifelse(is.na(prepreVAF), 0, prepreVAF),
preVAF = ifelse(is.na(preVAF), 0, preVAF),
duringVAF = ifelse(is.na(duringVAF), 0, duringVAF),
postVAF = ifelse(is.na(postVAF), 0, postVAF),
postpostVAF = ifelse(is.na(postpostVAF), 0, postpostVAF)
) %>%
filter(ifelse(Cohort == "Untreated", change_in_days < 350, TRUE)) # To keep the Untreated to be similar to the time frame of the treated
# Demographics Dataframe
g1_demo <- fread(paste0(data_file_path, "g1_demo.csv"))
g1_extra_demo <- fread(paste0(data_file_path, "g1_sra_extra_columns.csv"))
# Blood Counts Lab Results
g1_lab <- fread(paste0(data_file_path, "g1_labresults.csv"))# g1_demo %>%
# left_join(
# g1_extra_demo,
# by=c("DeID"="DEID", "SampleDeID")
# ) %>%
# mutate(
# SampleCollectionDate = case_when(
# !is.na(SampleCollectionDate) ~ as.Date(SampleCollectionDate),
# SampleDeID == "AP187910G01" ~ as.Date("2022-02-08"),
# ),
# sample_name = paste0(DeID, "_", SampleDeID),
# sample_title = paste0(DeID, "_", SampleDeID, "_", Cohort, "_", Trilaciclib),
# organism = "Homo sapien",
# isolate = paste0(Cohort, " ", Trilaciclib, " ", TimePoint, " ", whichdraw),
# age = ceiling(Age),
# biomaterial_provider = "Dr. Kelly Bolton",
# collection_date = SampleCollectionDate,
# geo_loc_name = "United States of America",
# sex = Sex,
# tissue = "Blood"
# ) %>%
# dplyr::select(
# sample_name, sample_title, organism, isolate, age, biomaterial_provider, collection_date, geo_loc_name, sex, tissue
# )
#fwrite("SRA_Upload_BioSample_Attributes.tsv", sep = "\t")1 Table 1
label(g1_demo$Age) <- "Age at Treatment Start"
label(g1_demo$Sex) <- "Gender"
label(g1_demo$Race) <- "Race"
label(g1_demo$SmokingStatus) <- "Smoking Status"
for_table <- g1_demo %>%
filter(ifelse(Cohort == "TNBC", whichdraw == "PreTx" | whichdraw == "MidTx", whichdraw == "PreTx" | whichdraw == "PostTx")) %>%
filter(DeID %in% c(g1_demo %>%
filter(ifelse(Cohort == "TNBC", whichdraw == "PreTx" | whichdraw == "MidTx", whichdraw == "PreTx" | whichdraw == "PostTx")) %>%
group_by(Cohort, DeID) %>%
summarise(n = n()) %>%
filter(n > 1) %>%
pull(DeID))
)## `summarise()` has grouped output by 'Cohort'. You can override using the
## `.groups` argument.table1(~ Age + Sex + Race + Ethnicity + SmokingStatus | Cohort + Trilaciclib, data = for_table %>% filter(whichdraw == "PreTx"))CRC |
SCLC |
TNBC |
Untreated |
Overall |
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Placebo (N=65) |
Trilaciclib (N=60) |
Placebo (N=37) |
Trilaciclib (N=28) |
Placebo (N=11) |
Trilaciclib (N=23) |
Untreated (N=176) |
Placebo (N=113) |
Trilaciclib (N=111) |
Untreated (N=176) |
|
| Age at Treatment Start | ||||||||||
| Mean (SD) | 54.7 (11.5) | 55.2 (11.6) | 64.9 (8.14) | 63.8 (8.33) | 60.8 (14.2) | 56.3 (11.1) | 65.2 (10.8) | 58.6 (11.7) | 57.6 (11.3) | 65.2 (10.8) |
| Median [Min, Max] | 55.0 [30.0, 79.0] | 55.5 [28.0, 74.0] | 66.0 [39.0, 83.0] | 65.5 [45.0, 78.0] | 57.0 [41.0, 86.0] | 55.0 [34.0, 74.0] | 66.8 [29.9, 87.9] | 59.0 [30.0, 86.0] | 60.0 [28.0, 78.0] | 66.8 [29.9, 87.9] |
| Gender | ||||||||||
| Female | 26 (40.0%) | 21 (35.0%) | 13 (35.1%) | 12 (42.9%) | 11 (100%) | 23 (100%) | 91 (51.7%) | 50 (44.2%) | 56 (50.5%) | 91 (51.7%) |
| Male | 39 (60.0%) | 39 (65.0%) | 24 (64.9%) | 16 (57.1%) | 0 (0%) | 0 (0%) | 85 (48.3%) | 63 (55.8%) | 55 (49.5%) | 85 (48.3%) |
| Race | ||||||||||
| Asian | 3 (4.6%) | 3 (5.0%) | 1 (2.7%) | 0 (0%) | 0 (0%) | 1 (4.3%) | 2 (1.1%) | 4 (3.5%) | 4 (3.6%) | 2 (1.1%) |
| Black | 5 (7.7%) | 2 (3.3%) | 0 (0%) | 0 (0%) | 3 (27.3%) | 1 (4.3%) | 27 (15.3%) | 8 (7.1%) | 3 (2.7%) | 27 (15.3%) |
| Unknown | 3 (4.6%) | 5 (8.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1.1%) | 3 (2.7%) | 5 (4.5%) | 2 (1.1%) |
| White | 54 (83.1%) | 48 (80.0%) | 35 (94.6%) | 28 (100%) | 8 (72.7%) | 20 (87.0%) | 145 (82.4%) | 97 (85.8%) | 96 (86.5%) | 145 (82.4%) |
| Other | 0 (0%) | 2 (3.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (4.3%) | 0 (0%) | 0 (0%) | 3 (2.7%) | 0 (0%) |
| American Indian or Alaska Native | 0 (0%) | 0 (0%) | 1 (2.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.9%) | 0 (0%) | 0 (0%) |
| Ethnicity | ||||||||||
| Hispanic or Latino | 7 (10.8%) | 7 (11.7%) | 1 (2.7%) | 0 (0%) | 0 (0%) | 2 (8.7%) | 0 (0%) | 8 (7.1%) | 9 (8.1%) | 0 (0%) |
| Not Hispanic or Latino | 55 (84.6%) | 48 (80.0%) | 35 (94.6%) | 28 (100%) | 11 (100%) | 21 (91.3%) | 170 (96.6%) | 101 (89.4%) | 97 (87.4%) | 170 (96.6%) |
| Unknown | 3 (4.6%) | 5 (8.3%) | 1 (2.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 6 (3.4%) | 4 (3.5%) | 5 (4.5%) | 6 (3.4%) |
| Smoking Status | ||||||||||
| Unknown | 65 (100%) | 60 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 16 (9.1%) | 65 (57.5%) | 60 (54.1%) | 16 (9.1%) |
| Current | 0 (0%) | 0 (0%) | 10 (27.0%) | 10 (35.7%) | 1 (9.1%) | 0 (0%) | 9 (5.1%) | 11 (9.7%) | 10 (9.0%) | 9 (5.1%) |
| Former | 0 (0%) | 0 (0%) | 26 (70.3%) | 18 (64.3%) | 2 (18.2%) | 6 (26.1%) | 0 (0%) | 28 (24.8%) | 24 (21.6%) | 0 (0%) |
| Never | 0 (0%) | 0 (0%) | 1 (2.7%) | 0 (0%) | 8 (72.7%) | 17 (73.9%) | 151 (85.8%) | 9 (8.0%) | 17 (15.3%) | 151 (85.8%) |
2 Plotting Variables
panel_theme_basic <- theme_bw() + theme(
panel.border = element_blank(),
legend.title = element_blank(),
legend.key.size = unit(5, "mm"),
legend.text = element_text(size = 22),
legend.position = "top",
legend.direction = "horizontal",
plot.subtitle = element_text(hjust = 0.5, size = 24),
plot.title = element_text(face = "bold", hjust = 0, vjust = -2, size = 24),
panel.grid.major = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 22),
axis.text.y = element_text(size = 22),
axis.text.x = element_text(size = 22),
axis.title = element_text(size = 24),
axis.line = element_line(colour = "black"),
plot.margin = margin(1, 1, 1, 1, "cm")
)
default_sig <- function(y_position, xmin, xmax, model_pvalue, model_stars, tip_length = 0.03, size = 1, textsize = 10, color = "black", show_values = TRUE, hide_ns = TRUE) {
if (show_values){
list(y_position = y_position, xmin = c(xmin), xmax = c(xmax), annotation = paste0("p = ", format(model_pvalue, digits = 5), model_stars), tip_length = tip_length, size = size, textsize = textsize, color = color)
} else {
if (model_stars == "") { model_stars <- "ns"}
if (hide_ns & model_stars == "ns") {
list(y_position = y_position, xmin = c(xmin), xmax = c(xmax), annotation = "", tip_length = 0, size = 0, textsize = 0, color = "black")
} else {
list(y_position = y_position, xmin = c(xmin), xmax = c(xmax), annotation = model_stars, tip_length = tip_length, size = size, textsize = textsize, color = color)
}
}
}
default_sig_data <- function(data, y_position, xmin, xmax, model_pvalue, model_stars, tip_length = 0.03, size = 1, textsize = 10, color = "black", fill = "black", show_values = TRUE, hide_ns = TRUE) {
if (show_values) {
list(data = data, y_position = y_position, xmin = c(xmin), xmax = c(xmax), annotation = paste0("p = ", format(model_pvalue, digits = 5), model_stars), tip_length = tip_length, size = size, textsize = textsize, color = color, fill = fill)
} else {
if (model_stars == "") { model_stars <- "ns"}
if (hide_ns & model_stars == "ns") {
list(data = data, y_position = y_position, xmin = c(xmin), xmax = c(xmax), annotation = "", tip_length = 0, size = 0, textsize = 0, color = "black", fill = "black")
} else {
list(data = data, y_position = y_position, xmin = c(xmin), xmax = c(xmax), annotation = model_stars, tip_length = tip_length, size = size, textsize = textsize, color = color, fill = fill)
}
}
}
add_significance_annotation <- function(p, label, pos, start, end, size_text = 10, size_line = 1) {
p +
annotate("text", x = (pos + 0.1), y = (start + end)/2, label = label, size = size_text) +
annotate("segment", x = pos, xend = pos, y = start, yend = end, size = size_line) +
annotate("segment", x = pos - 0.05, xend = pos + 0.008, y = start, yend = start, size = size_line) +
annotate("segment", x = pos - 0.05, xend = pos + 0.008, y = end, yend = end, size = size_line)
}
signif.num <- function(x, ns = FALSE) {
if (ns) {
symbols = c("***", "**", "*", "ns")
} else {
symbols = c("***", "**", "*", "")
}
symnum(unlist(x), corr = FALSE, na = FALSE, legend = T,
cutpoints = c(0, 0.001, 0.01, 0.05, 1),
symbols = symbols)
}3 Default Variables
gene_list = c("DNMT3A", "TET2", "ASXL1", "TP53", "PPM1D", "CHEK2", "JAK2", "SRSF2", "SF3B1")
DDR_genes = c("TP53", "PPM1D", "CHEK2")
treatment_colors = c('Untreated' = '#C0C0C0', 'Placebo' = '#7AD3F7', 'Trilaciclib' = '#F37B79')
treatment_colors = c('Untreated' = '#BC3C29FF', 'Placebo' = '#E18727FF', 'Trilaciclib' = '#20854EFF')4 Basic Processing - Merging ALL Information Together
g1_tp <- g1_tp %>%
left_join(
g1_df %>%
dplyr::select(
key, Gene,
VariantClass, gene_aachange, oncoKB, oncoKB_reviewed,
n.loci.vep, source.totals.loci, n.loci.truncating.vep, source.totals.loci.truncating, n.HGVSp, source.totals.p, n.HGVSc, source.totals.c, CosmicCount, heme_cosmic_count, myeloid_cosmic_count
) %>%
rowwise() %>%
mutate(non_na_count = sum(across(everything(), ~ !is.na(.)))) %>%
ungroup() %>%
group_by(key, Gene) %>%
slice_max(order_by = non_na_count, with_ties = FALSE) %>%
ungroup() %>%
dplyr::select(-non_na_count),
by = c("key", "Gene")
)
g1_tp <- g1_tp %>%
left_join(
g1_demo %>%
filter(whichdraw == "PreTx") %>%
dplyr::select(DeID, Age, Sex, Race, Ethnicity, SmokingStatus)
)
g1_lab_changes <- g1_lab %>%
pivot_wider(
id_cols = c(Cohort, subject),
names_from = c(Testcode, whichdraw),
values_from = Labvalue
) %>%
mutate(
perc_BASO_of_WBC_PreTx = BASO_PreTx / WBC_PreTx,
perc_EOS_of_WBC_PreTx = EOS_PreTx / WBC_PreTx,
perc_LYM_of_WBC_PreTx = LYM_PreTx / WBC_PreTx,
perc_MONO_of_WBC_PreTx = MONO_PreTx / WBC_PreTx,
perc_NEUT_of_WBC_PreTx = NEUT_PreTx / WBC_PreTx,
perc_BASO_of_WBC_PostTx = BASO_PostTx / WBC_PostTx,
perc_EOS_of_WBC_PostTx = EOS_PostTx / WBC_PostTx,
perc_LYM_of_WBC_PostTx = LYM_PostTx / WBC_PostTx,
perc_MONO_of_WBC_PostTx = MONO_PostTx / WBC_PostTx,
perc_NEUT_of_WBC_PostTx = NEUT_PostTx / WBC_PostTx,
perc_BASO_of_WBC_MidTx = BASO_MidTx / WBC_MidTx,
perc_EOS_of_WBC_MidTx = EOS_MidTx / WBC_MidTx,
perc_LYM_of_WBC_MidTx = LYM_MidTx / WBC_MidTx,
perc_MONO_of_WBC_MidTx = MONO_MidTx / WBC_MidTx,
perc_NEUT_of_WBC_MidTx = NEUT_MidTx / WBC_MidTx,
change_in_prop_BASO = case_when(
Cohort == "SCLC" ~ perc_BASO_of_WBC_PostTx - perc_BASO_of_WBC_PreTx,
Cohort == "TNBC" ~ perc_BASO_of_WBC_MidTx - perc_BASO_of_WBC_PreTx,
Cohort == "CRC" ~ perc_BASO_of_WBC_PostTx - perc_BASO_of_WBC_PreTx
),
change_in_prop_EOS = case_when(
Cohort == "SCLC" ~ perc_EOS_of_WBC_PostTx - perc_EOS_of_WBC_PreTx,
Cohort == "TNBC" ~ perc_EOS_of_WBC_MidTx - perc_EOS_of_WBC_PreTx,
Cohort == "CRC" ~ perc_EOS_of_WBC_PostTx - perc_EOS_of_WBC_PreTx
),
change_in_prop_NEUT = case_when(
Cohort == "SCLC" ~ perc_NEUT_of_WBC_PostTx - perc_NEUT_of_WBC_PreTx,
Cohort == "TNBC" ~ perc_NEUT_of_WBC_MidTx - perc_NEUT_of_WBC_PreTx,
Cohort == "CRC" ~ perc_NEUT_of_WBC_PostTx - perc_NEUT_of_WBC_PreTx,
),
change_in_prop_LYM = case_when(
Cohort == "SCLC" ~ perc_LYM_of_WBC_PostTx - perc_LYM_of_WBC_PreTx,
Cohort == "TNBC" ~ perc_LYM_of_WBC_MidTx - perc_LYM_of_WBC_PreTx,
Cohort == "CRC" ~ perc_LYM_of_WBC_PostTx - perc_LYM_of_WBC_PreTx
),
change_in_prop_MONO = case_when(
Cohort == "SCLC" ~ perc_MONO_of_WBC_PostTx - perc_MONO_of_WBC_PreTx,
Cohort == "TNBC" ~ perc_MONO_of_WBC_MidTx - perc_MONO_of_WBC_PreTx,
Cohort == "CRC" ~ perc_MONO_of_WBC_PostTx - perc_MONO_of_WBC_PreTx
),
change_in_prop_GRAN = rowSums(across(c(change_in_prop_BASO, change_in_prop_EOS, change_in_prop_NEUT)), na.rm = TRUE)
)
g1_tp <- g1_tp %>%
left_join(
g1_lab_changes %>%
dplyr::select(Cohort, subject,
change_in_prop_BASO, change_in_prop_EOS, change_in_prop_LYM, change_in_prop_MONO, change_in_prop_NEUT, change_in_prop_GRAN
),
by = c("DeID"="subject", "Cohort")
)
# Simplifying Race, Ethnicity Variables
g1_tp <- g1_tp %>%
mutate(
Race = case_when(
Race == "White" ~ "White",
Race == "Unknown" ~ "White",
TRUE ~ "Non-White"
),
Race = case_when(
Race == "White" & Ethnicity == "Hispanic or Latino" ~ "Non-White",
Race == "White" & Ethnicity == "Not Hispanic or Latino" ~ "White",
Race == "White" & Ethnicity == "Unknown" ~ "White",
Race == "Non-White" & Ethnicity == "Hispanic or Latino" ~ "Non-White",
Race == "Non-White" & Ethnicity == "Not Hispanic or Latino" ~ "Non-White",
Race == "Non-White" & Ethnicity == "Unknown" ~ "Non-White"
)
)5 Reference Variables
g1_tp$Trilaciclib <- factor(g1_tp$Trilaciclib, levels = c("Untreated", "Trilaciclib", "Placebo"))
g1_tp$Gene_Class <- factor(g1_tp$Gene_Class, levels = c("DTA", "DDR"))
g1_tp$Gene <- factor(g1_tp$Gene, levels = c("TP53", "PPM1D", "CHEK2", "DNMT3A", "TET2", "ASXL1", "SF3B1", "SRSF2", "JAK2"))
g1_tp$Cohort <- factor(g1_tp$Cohort, levels = c("Untreated", "SCLC", "TNBC", "CRC"))
g1_tp$Sex <- factor(g1_tp$Sex, levels = c("Male", "Female"))
#g1_tp$Race <- factor(g1_tp$Race, levels = c("White", "Black", "Asian", "American Indian or Alaska Native", "Other", "Unknown"))
g1_tp$Race <- factor(g1_tp$Race, levels = c("White", "Non-White"))
#g1_tp$Ethnicity <- factor(g1_tp$Ethnicity, levels = c("Hispanic or Latino", "Not Hispanic or Latino", "Unknown"))
g1_tp$SmokingStatus <- factor(g1_tp$SmokingStatus, levels = c("Never", "Former", "Current", "Unknown"))6 Mutation Table - SCLC and Untreated ONLY
num_mutations_per_person <- g1_df %>%
filter(Cohort == "SCLC" | Cohort == "Untreated") %>%
filter(average_AF >= 0.002) %>% # Anything less than this may be noise.
group_by(DeID, Gene, Trilaciclib, whichdraw) %>%
summarise(n = sum(putative_driver)) %>%
#mutate(Gene = ifelse(Gene == "", "No Mutation", Gene)) %>%
pivot_wider(names_from = Gene, values_from = n, values_fill = 0) %>%
ungroup() #%>%
# mutate(
# DDR_Mutations = rowSums(.[, c("PPM1D", "TP53", "CHEK2")], na.rm = TRUE),
# )
g1_mut_table <- g1_demo %>%
filter(Cohort == "SCLC" | Cohort == "Untreated") %>%
dplyr::select(DeID, Trilaciclib, Age, whichdraw) %>%
left_join(num_mutations_per_person) %>%
group_by(DeID, Trilaciclib, Age, whichdraw) %>%
summarise_if(is.numeric, function(x) sum(x != 0, na.rm = TRUE))
g1_mut_table %>%
filter(whichdraw == "PreTx") %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0)
) %>%
group_by(Trilaciclib) %>%
summarise(n_ch = sum(CH_Binary), n_total = n())| Trilaciclib | n_ch | n_total |
|---|---|---|
| Placebo | 26 | 37 |
| Trilaciclib | 20 | 28 |
| Untreated | 127 | 176 |
7 Mutation Table - TNBC and CRC
num_mutations_per_person_tnbc <- g1_df %>%
filter(Cohort == "TNBC" | Cohort == "Untreated") %>%
filter(average_AF >= 0.002) %>%
group_by(DeID, Gene, Trilaciclib, whichdraw) %>%
summarise(n = sum(putative_driver)) %>%
pivot_wider(names_from = Gene, values_from = n, values_fill = 0) %>%
ungroup()
g1_mut_table_tnbc <- g1_demo %>%
filter(Cohort == "TNBC" | Cohort == "Untreated") %>%
dplyr::select(DeID, Trilaciclib, Age, whichdraw) %>%
left_join(num_mutations_per_person_tnbc) %>%
group_by(DeID, Trilaciclib, Age, whichdraw) %>%
summarise_if(is.numeric, function(x) sum(x != 0, na.rm = TRUE))
num_mutations_per_person_crc <- g1_df %>%
filter(Cohort == "CRC" | Cohort == "Untreated") %>%
filter(average_AF >= 0.002) %>%
group_by(DeID, Gene, Trilaciclib, whichdraw) %>%
summarise(n = sum(putative_driver)) %>%
pivot_wider(names_from = Gene, values_from = n, values_fill = 0) %>%
ungroup()
g1_mut_table_crc <- g1_demo %>%
filter(Cohort == "CRC" | Cohort == "Untreated") %>%
dplyr::select(DeID, Trilaciclib, Age, whichdraw) %>%
left_join(num_mutations_per_person_crc) %>%
group_by(DeID, Trilaciclib, Age, whichdraw) %>%
summarise_if(is.numeric, function(x) sum(x != 0, na.rm = TRUE))8 Figure 1a - SCLC Schematic
fig1a <- ggdraw() + draw_image(sclc_schematic)
fig1a
9 Figure 1B - Ribbon Plot
fig1b <- g1_mut_table %>%
filter(whichdraw == "PreTx", Age >= 45, Age <= 85) %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0),
Treatment = ifelse(Trilaciclib == "Untreated", "Healthy Control", "SCLC Pre-Treatment"),
Treatment = factor(Treatment, levels = c("Healthy Control", "SCLC Pre-Treatment"))
) %>%
ggplot(
aes(
x = Age,
y = CH_Binary,
fill = Treatment
)
) +
geom_smooth(
aes(
fill = Treatment,
color = Treatment
),
method = "gam",
formula = y ~ s(x),
method.args = list(family = "binomial"),
size = 1.5,
se = TRUE,
alpha = 0.1
) +
scale_y_continuous(labels = scales::label_percent(accuracy = 1L), expand = expansion(add = c(0.01, 0.01))) +
scale_x_continuous(breaks = seq(0, 90, by = 5)) +
labs(x = "Age", y = "Frequency (%)") +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
#scale_fill_manual(values = treatment_colors) +
#scale_color_manual(values = treatment_colors) +
theme(
plot.margin = margin(0, 1, 1, 1, "cm")
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.fig1b
ggsave(paste0(figures_save_path, "Figure1B.png"), plot = fig1b, width = 14, height = 8, dpi = 300)10 Figure 1C - Waterfall Plot
# g1_tp %>% filter(Cohort == "SCLC" | Cohort == "Untreated") %>%
# filter(
# all_time_points == TRUE |
# (all_time_points == FALSE & called == TRUE) |
# (all_time_points == FALSE & called == FALSE & preVAF >= 0.001)
# )
df_to_maf <- g1_df %>% filter(Cohort == "SCLC" | Cohort == "Untreated") %>%
filter(VariantClass != "synonymous_variant", putative_driver == 1, whichdraw == "PreTx") %>%
tidyr::separate(key, into= c("Chromosome", "Start_Position", "Reference_Allele", "Tumor_Seq_Allele2"), sep = ":") %>%
mutate(
Tumor_Sample_Barcode = DeID,
Hugo_Symbol = Gene,
Variant_Classification = VariantClass,
Start_Position = as.numeric(Start_Position),
Variant_Type = case_when(
nchar(Reference_Allele) == nchar(Tumor_Seq_Allele2) ~ "SNP",
nchar(Reference_Allele) < nchar(Tumor_Seq_Allele2) ~ "INS",
nchar(Reference_Allele) > nchar(Tumor_Seq_Allele2) ~ "DEL"
),
End_Position = case_when(
Variant_Type == "SNP" ~ Start_Position,
Variant_Type == "INS" ~ Start_Position,
Variant_Type == "DEL" ~ (Start_Position + nchar(Reference_Allele) - 1)
),
stringsAsFactors = FALSE
)
clinical_data <- g1_df %>%
mutate(
Tumor_Sample_Barcode = DeID,
Cohort = ifelse(Cohort == "Untreated", "Healthy Control", "SCLC"),
Cohort = factor(Cohort, levels = c("SCLC", "Healthy Control")),
Treatment = ifelse(Trilaciclib == "Untreated", "Healthy Control", Trilaciclib),
Treatment = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib")),
stringAsFactors = FALSE
)
maf_to_plot <- maftools::read.maf(maf = df_to_maf, vc_nonSyn = names(table(df_to_maf$VariantClass)), clinicalData = clinical_data)## -Validating
## --Non MAF specific values in Variant_Classification column:
## missense_variant
## frameshift_variant
## stop_gained
## splice_region_variant
## inframe_insertion
## inframe_deletion
## -Summarizing
## -Processing clinical data
## -Finished in 0.149s elapsed (0.129s cpu)maftools::oncoplot(maf = maf_to_plot, genes = gene_list, clinicalFeatures = c("Cohort", "Treatment"))
11 Figure 1D - VAF Bin Mutation Bar Plot
df_to_plot <- g1_df %>% filter(Cohort == "SCLC" | Cohort == "Untreated") %>%
filter(whichdraw == "PreTx") %>%
mutate(
vaf_bin = case_when(
average_AF >= 0.002 & average_AF < 0.01 ~ "0.2-1% VAF",
average_AF >= 0.01 & average_AF < 0.05 ~ "1-5% VAF",
average_AF >= 0.05 ~ ">=5% VAF",
average_AF >= 0.001 & average_AF < 0.002 ~ "0.1-0.2% VAF"
)
) %>%
filter(vaf_bin != "<0.2% VAF") %>%
group_by(Gene, vaf_bin) %>%
summarise(n = n()) %>%
group_by(Gene) %>%
mutate(
total = sum(n),
percentage = (n / total) * 100,
#vaf_bin = factor(vaf_bin, levels = c("0.1-0.2% VAF", "0.2-1% VAF", "1-5% VAF", ">=5% VAF")),
vaf_bin = factor(vaf_bin, levels = c(">=5% VAF", "1-5% VAF", "0.2-1% VAF", "0.1-0.2% VAF")),
Gene = factor(Gene, levels = c(gene_list))
)## `summarise()` has grouped output by 'Gene'. You can override using the
## `.groups` argument.fig1d <- df_to_plot %>%
ggplot(
aes(
x = Gene,
y = percentage,
fill = vaf_bin
)
) +
geom_bar(stat = "identity") +
labs(
x = "Gene",
y = "Percentage of Mutations at Baseline",
fill = "VAF Bin"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
#axis.text.y = element_text(angle = 90, hjust = 1)
plot.margin = margin(2, 1, 1, 1, "cm")
) +
scale_fill_nejm()
fig1d
ggsave(paste0(figures_save_path, "Figure1D.png"), plot = fig1d, width = 10, height = 8, dpi = 300)12 Figure 1E - Proportion of Patients with CH Mutations
model_treatment_gene <- map_dfr(c(gene_list), function(gene){
rbind(
glm(
formula = as.formula(paste(gene, "~ Trilaciclib + Age")),
family = binomial(link = "logit"),
data = g1_mut_table %>%
filter(whichdraw == "PostTx") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib")))
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(Gene = gene),
glm(
formula = as.formula(paste(gene, "~ Trilaciclib + Age")),
family = binomial(link = "logit"),
data = g1_mut_table %>%
filter(whichdraw == "PostTx") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo")))
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(Gene = gene)
)
})
model_treatment_gene <- model_treatment_gene %>% filter(term != "Age")
g1_mut_table <- g1_mut_table %>%
mutate(
DTA_Mutations = ifelse(rowSums(across(c("DNMT3A", "TET2", "ASXL1")), na.rm = TRUE) > 0, 1, 0),
DDR_Mutations = ifelse(rowSums(across(c("PPM1D", "TP53", "CHEK2")), na.rm = TRUE) > 0, 1, 0),
Treatment = ifelse(Trilaciclib == "Untreated", "Healthy Control", "SCLC Post-Treatment"),
Treatment = factor(Treatment, levels = c("Healthy Control", "SCLC Post-Treatment"))
)
g1_mut_table %>%
filter(whichdraw == "PostTx") %>%
group_by(Trilaciclib) %>%
summarise(n_ch = sum(DDR_Mutations), n_total = n())| Trilaciclib | n_ch | n_total |
|---|---|---|
| Placebo | 21 | 37 |
| Trilaciclib | 8 | 28 |
| Untreated | 42 | 178 |
glm(
formula = as.formula("DDR_Mutations ~ Treatment + Age"),
family = binomial(link = "logit"),
data = g1_mut_table %>%
filter(whichdraw == "PostTx")
) %>% sjPlot::get_model_data(type = "est")| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TreatmentSCLC Post-Treatment | 3.405394 | 0.3304140 | 0.95 | 1.791850 | 6.574561 | 3.708561 | Inf | 0.0002084 | *** | 3.41 *** | pos | 2 | 1.825 | 2.175 |
| Age | 1.078908 | 0.0182065 | 0.95 | 1.042594 | 1.119981 | 4.171552 | Inf | 0.0000303 | *** | 1.08 *** | pos | 1 | 0.825 | 1.175 |
fig1e <- g1_mut_table %>%
filter(whichdraw == "PostTx") %>%
group_by(Treatment) %>%
dplyr::select(-Age, -Trilaciclib, -whichdraw, -DTA_Mutations, -DDR_Mutations) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
pivot_longer(cols = -c(Treatment), names_to = "Gene", values_to = "n") %>%
group_by(Treatment, Gene) %>%
left_join(
g1_mut_table %>%
filter(whichdraw == "PostTx") %>%
group_by(Treatment) %>%
summarise(total_patients = n())
) %>%
mutate(
Proportion = n / total_patients,
Gene = factor(Gene, levels = c(gene_list)),
Treatment = factor(Treatment, levels = c("Healthy Control", "SCLC Post-Treatment"))
#Trilaciclib = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))
) %>%
ggplot(
aes(
x = Gene,
y = Proportion,
fill = Treatment
)
) +
geom_bar(color = "black", stat = "identity", position = position_dodge()) +
labs(
x = "",
y = "Percent of Patients\nwith Mutated Gene\n Post-Treatment",
fill = "Treatment"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = c(0.7, 0.8),
legend.direction = "vertical",
plot.margin = margin(2, 0, 1, 1, "cm")
) +
scale_y_continuous(labels = scales::percent, limits = c(NA, 0.6), expand = c(0, 0)) +
scale_fill_nejm() +
scale_color_nejm()## Joining with `by = join_by(Treatment)`## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated. #scale_fill_manual(values = treatment_colors)
# do.call(geom_signif, default_sig(0.6, 0.75, 1, model_treatment_gene$p.value[1], model_treatment_gene$p.stars[1], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.65, 0.75, 1.25, model_treatment_gene$p.value[2], model_treatment_gene$p.stars[2], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.7, 1, 1.25, model_treatment_gene$p.value[3], model_treatment_gene$p.stars[3], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.35, 1.75, 2, model_treatment_gene$p.value[4], model_treatment_gene$p.stars[4], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.4, 1.75, 2.25, model_treatment_gene$p.value[5], model_treatment_gene$p.stars[5], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.45, 2, 2.25, model_treatment_gene$p.value[6], model_treatment_gene$p.stars[6], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.2, 2.75, 3, model_treatment_gene$p.value[7], model_treatment_gene$p.stars[7], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.25, 2.75, 3.25, model_treatment_gene$p.value[8], model_treatment_gene$p.stars[8], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.3, 3, 3.25, model_treatment_gene$p.value[9], model_treatment_gene$p.stars[9], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.45, 3.75, 4, model_treatment_gene$p.value[10], model_treatment_gene$p.stars[10], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.52, 3.75, 4.25, model_treatment_gene$p.value[11], model_treatment_gene$p.stars[11], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.6, 4, 4.25, model_treatment_gene$p.value[12], model_treatment_gene$p.stars[12], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.45, 4.75, 5, model_treatment_gene$p.value[13], model_treatment_gene$p.stars[13], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.52, 4.75, 5.25, model_treatment_gene$p.value[14], model_treatment_gene$p.stars[14], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.6, 5, 5.25, model_treatment_gene$p.value[15], model_treatment_gene$p.stars[15], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.25, 5.75, 6, model_treatment_gene$p.value[16], model_treatment_gene$p.stars[16], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.32, 5.75, 6.25, model_treatment_gene$p.value[17], model_treatment_gene$p.stars[17], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.4, 6, 6.25, model_treatment_gene$p.value[18], model_treatment_gene$p.stars[18], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig(0.1, 6.75, 7, model_treatment_gene$p.value[19], model_treatment_gene$p.stars[19], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.15, 6.75, 7.25, model_treatment_gene$p.value[20], model_treatment_gene$p.stars[20], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.2, 7, 7.25, model_treatment_gene$p.value[21], model_treatment_gene$p.stars[21], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.1, 7.75, 8, model_treatment_gene$p.value[22], model_treatment_gene$p.stars[22], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.15, 7.75, 8.25, model_treatment_gene$p.value[23], model_treatment_gene$p.stars[23], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.2, 8, 8.25, model_treatment_gene$p.value[24], model_treatment_gene$p.stars[24], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.1, 8.75, 9, model_treatment_gene$p.value[25], model_treatment_gene$p.stars[25], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.15, 8.75, 9.25, model_treatment_gene$p.value[26], model_treatment_gene$p.stars[26], show_values = FALSE)) +
# do.call(geom_signif, default_sig(0.2, 9, 9.25, model_treatment_gene$p.value[27], model_treatment_gene$p.stars[27], show_values = FALSE)) #+
#do.call(geom_signif, default_sig(0.6, 9.75, 10, model_treatment_gene$p.value[28], model_treatment_gene$p.stars[28], show_values = FALSE, hide_ns = FALSE)) +
#do.call(geom_signif, default_sig(0.65, 9.75, 10.25, model_treatment_gene$p.value[29], model_treatment_gene$p.stars[29], show_values = FALSE, hide_ns = FALSE)) +
#do.call(geom_signif, default_sig(0.7, 10, 10.25, model_treatment_gene$p.value[30], model_treatment_gene$p.stars[30], show_values = FALSE, hide_ns = FALSE))
fig1e
ggsave(paste0(figures_save_path, "Figure1E.png"), plot = fig1e, width = 10, height = 6, dpi = 300)13 Figure 1E - Proportion of Patients with CH Mutations Continued…
model_treatment_gene_class <- map_dfr(c("DTA_Mutations", "DDR_Mutations"), function(gene_class){
rbind(
glm(
formula = as.formula(paste(gene_class, "~ Trilaciclib + Age")),
family = binomial(link = "logit"),
data = g1_mut_table %>%
filter(whichdraw == "PostTx") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo")))
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(
GeneClass = ifelse(gene_class == "DTA_Mutations", "DTA", "DDR"))
)
})
model_treatment_gene_class <- model_treatment_gene_class %>% filter(term != "Age")
fig1e_cont <- g1_mut_table %>%
filter(whichdraw == "PostTx", Trilaciclib != "Untreated") %>%
group_by(Trilaciclib) %>%
dplyr::select(Trilaciclib, DTA_Mutations, DDR_Mutations) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
pivot_longer(cols = -c(Trilaciclib), names_to = "Gene", values_to = "n") %>%
left_join(
g1_mut_table %>%
filter(whichdraw == "PostTx", Trilaciclib != "Untreated") %>%
group_by(Trilaciclib) %>%
summarise(total_patients = n())
) %>%
mutate(
Proportion = n / total_patients,
Trilaciclib = factor(Trilaciclib, levels = c("Placebo", "Trilaciclib")),
Gene = ifelse(Gene == "DTA_Mutations", "DTA", "DDR"),
Gene = factor(Gene, levels = c("DTA", "DDR"))
) %>%
ggplot(
aes(
x = Gene,
y = Proportion,
fill = Trilaciclib
)
) +
geom_bar(color = "black", stat = "identity", position = position_dodge()) +
labs(
x = "",
y = "Percent of Patients\nwith Mutated Gene\nPost-Treatment",
fill = "Trilaciclib"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = c(0.85, 0.6),
legend.direction = "vertical",
#plot.margin = margin(1, 0, 1, 1, "cm")
) +
scale_y_continuous(labels = scales::percent, limits = c(NA, 0.75), expand = c(0, 0)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig(0.65, 0.75, 1.25, model_treatment_gene_class$p.value[1], model_treatment_gene_class$p.stars[1], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig(0.65, 1.75, 2.25, model_treatment_gene_class$p.value[2], model_treatment_gene_class$p.stars[2], show_values = FALSE))## Joining with `by = join_by(Trilaciclib)`fig1e_cont
ggsave(paste0(figures_save_path, "Figure1E_Continued.png"), plot = fig1e_cont, width = 10, height = 6, dpi = 300)14 Figure 1F - Small Cell Lung Cancer Growth Rate Plot
df_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1"
)
)
model_growth_rate_class_geeglm <- rbind(
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
mutate(
preAge = as.numeric(preAge)
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DDR"
),
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(
preAge = as.numeric(preAge),
Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DDR"
),
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
mutate(
preAge = as.numeric(preAge)
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DTA"
),
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(
preAge = as.numeric(preAge),
Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DTA"
)
)## Warning in diff(as.numeric(id)): NAs introduced by coercion
## Warning in diff(as.numeric(id)): NAs introduced by coercionmodel_growth_rate_class <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)
model_growth_rate_class <- model_growth_rate_class %>%
filter(term == "UntreatedVSTrilaciclib" | term == "UntreatedVSPlacebo" | term == "TrilaciclibVSPlacebo")
df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
group_by(Trilaciclib) %>%
summarise(n = mean(growth_rate))| Trilaciclib | n |
|---|---|
| Trilaciclib | 0.0100532 |
| Placebo | 0.0152614 |
counts <- df_to_plot %>%
filter(growth_rate != Inf & growth_rate != -Inf) %>%
filter(Gene_Class == "DTA" | Gene_Class == "DDR") %>%
group_by(Trilaciclib, Gene_Class) %>%
summarise(n = n(), .groups = "drop")
fig1f <- df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene_Class == "DTA" | Gene_Class == "DDR") %>% # We are filtering the data to only include the DDR and DTA gene classes.
ggplot(
aes(
x = Gene_Class, # Notice how we are using the Gene_Class variable for the x-axis where before we were using Trilaciclib.
y = growth_rate,
fill = Trilaciclib # We will still group data by Trilaciclib, but our main focus is on the Gene_Class variable.
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
geom_text(data = counts, aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = ""
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 5, 0.75, 1, model_growth_rate_class$p.value[1], model_growth_rate_class$p.stars[1], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 6, 0.75, 1.25, model_growth_rate_class$p.value[2], model_growth_rate_class$p.stars[2], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 7, 1, 1.25, model_growth_rate_class$p.value[3], model_growth_rate_class$p.stars[3], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 5, 1.75, 2, model_growth_rate_class$p.value[4], model_growth_rate_class$p.stars[4], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 6, 1.75, 2.25, model_growth_rate_class$p.value[5], model_growth_rate_class$p.stars[5], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 7, 2, 2.25, model_growth_rate_class$p.value[6], model_growth_rate_class$p.stars[6], show_values = FALSE))## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`fig1f## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_signif()`).## Warning: Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
ggsave(paste0(figures_save_path, "Figure1F.png"), plot = fig1f, width = 12, height = 10, dpi = 300)## Warning: Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).15 Figure 2C - Colorectal Cancer Growth Rate Plot
df_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance"
)
)
model_growth_rate_class_geeglm <- rbind(
geeglm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
#mutate(
# preAge = as.numeric(preAge)
#) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DDR"
),
geeglm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(
#preAge = as.numeric(preAge),
Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DDR"
),
geeglm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
#mutate(
# preAge = as.numeric(preAge)
#) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DTA"
),
geeglm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(
#preAge = as.numeric(preAge),
Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DTA"
)
)## Warning in diff(as.numeric(id)): NAs introduced by coercion
## Warning in diff(as.numeric(id)): NAs introduced by coercion
## Warning in diff(as.numeric(id)): NAs introduced by coercion
## Warning in diff(as.numeric(id)): NAs introduced by coercionmodel_growth_rate_class <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)
model_growth_rate_class <- model_growth_rate_class %>%
filter(term == "UntreatedVSTrilaciclib" | term == "UntreatedVSPlacebo" | term == "TrilaciclibVSPlacebo")
counts <- df_to_plot %>%
filter(growth_rate != Inf & growth_rate != -Inf) %>%
filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>%
group_by(Trilaciclib, Gene_Class) %>%
summarise(n = n(), .groups = "drop")
fig2c <- df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>% # We are filtering the data to only include the DDR and DTA gene classes.
ggplot(
aes(
x = Gene_Class, # Notice how we are using the Gene_Class variable for the x-axis where before we were using Trilaciclib.
y = growth_rate,
fill = Trilaciclib # We will still group data by Trilaciclib, but our main focus is on the Gene_Class variable.
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
geom_text(data = counts, aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = ""
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 5, 0.75, 1, model_growth_rate_class$p.value[1], model_growth_rate_class$p.stars[1], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 6, 0.75, 1.25, model_growth_rate_class$p.value[2], model_growth_rate_class$p.stars[2], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 7, 1, 1.25, model_growth_rate_class$p.value[3], model_growth_rate_class$p.stars[3], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 5, 1.75, 2, model_growth_rate_class$p.value[4], model_growth_rate_class$p.stars[4], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 6, 1.75, 2.25, model_growth_rate_class$p.value[5], model_growth_rate_class$p.stars[5], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 7, 2, 2.25, model_growth_rate_class$p.value[6], model_growth_rate_class$p.stars[6], show_values = FALSE))## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`fig2c
ggsave(paste0(figures_save_path, "Figure2C.png"), plot = fig2c, width = 12, height = 8, dpi = 300)16 Figure 2D - Triple Negative Breast Cancer Growth Rate Plot
df_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1"
)
)
model_growth_rate_class_geeglm <- rbind(
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
mutate(
preAge = as.numeric(preAge)
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DDR"
),
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(
preAge = as.numeric(preAge),
Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DDR"
),
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
mutate(
preAge = as.numeric(preAge)
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DTA"
),
geeglm(
formula = growth_rate ~ Trilaciclib + preAge,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(
preAge = as.numeric(preAge),
Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf"),
id = DeID,
corstr = "exchangeable"
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "preAge" ~ "Age"
),
Gene_Class = "DTA"
)
)## Warning in diff(as.numeric(id)): NAs introduced by coercion
## Warning in diff(as.numeric(id)): NAs introduced by coercionmodel_growth_rate_class <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)
model_growth_rate_class <- model_growth_rate_class %>%
filter(term == "UntreatedVSTrilaciclib" | term == "UntreatedVSPlacebo" | term == "TrilaciclibVSPlacebo")
counts <- df_to_plot %>%
filter(growth_rate != Inf & growth_rate != -Inf) %>%
filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>%
group_by(Trilaciclib, Gene_Class) %>%
summarise(n = n(), .groups = "drop")
fig2d <- df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>% # We are filtering the data to only include the DDR and DTA gene classes.
ggplot(
aes(
x = Gene_Class, # Notice how we are using the Gene_Class variable for the x-axis where before we were using Trilaciclib.
y = growth_rate,
fill = Trilaciclib # We will still group data by Trilaciclib, but our main focus is on the Gene_Class variable.
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
geom_text(data = counts, aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = ""
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 5, 0.75, 1, model_growth_rate_class$p.value[1], model_growth_rate_class$p.stars[1], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 6, 0.75, 1.25, model_growth_rate_class$p.value[2], model_growth_rate_class$p.stars[2], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DTA"), 7, 1, 1.25, model_growth_rate_class$p.value[3], model_growth_rate_class$p.stars[3], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 5, 1.75, 2, model_growth_rate_class$p.value[4], model_growth_rate_class$p.stars[4], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 6, 1.75, 2.25, model_growth_rate_class$p.value[5], model_growth_rate_class$p.stars[5], show_values = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene_Class == "DDR"), 7, 2, 2.25, model_growth_rate_class$p.value[6], model_growth_rate_class$p.stars[6], show_values = FALSE))## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`fig2d
ggsave(paste0(figures_save_path, "Figure2D.png"), plot = fig2d, width = 12, height = 8, dpi = 300)17 Figure 2E - Genes
df_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
mutate(Variant_Classification = if_else(VariantClass == "missense_variant", "Missense", "Nonsense"))
model_growth_rate_gene <- map_dfr(c("TP53", "PPM1D", "CHEK2"), function(gene) {
print(gene)
model_growth_rate_gene <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age,
data = df_to_plot %>%
filter(Gene == gene) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
gene = gene
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort + Age,
data = df_to_plot %>%
filter(Gene == gene) %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
gene = gene
)
)
model_growth_rate_gene
})## [1] "TP53"
## [1] "PPM1D"
## [1] "CHEK2"model_growth_rate_gene| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | gene |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UntreatedVSTrilaciclib | 0.0033874 | 0.0019547 | 0.95 | -0.0004438 | 0.0072186 | 1.7329229 | 138 | 0.0831094 | 0.00 | pos | 3 | 2.825 | 3.175 | TP53 | |
| UntreatedVSPlacebo | 0.0059025 | 0.0019497 | 0.95 | 0.0020812 | 0.0097238 | 3.0274252 | 138 | 0.0024665 | ** | 0.01 ** | pos | 2 | 1.825 | 2.175 | TP53 |
| Age | 0.0001009 | 0.0000446 | 0.95 | 0.0000136 | 0.0001883 | 2.2640330 | 138 | 0.0235721 | * | 0.00 * | pos | 1 | 0.825 | 1.175 | TP53 |
| TrilaciclibVSPlacebo | 0.0026568 | 0.0012011 | 0.95 | 0.0003027 | 0.0050108 | 2.2119820 | 123 | 0.0269679 | * | 0.00 * | pos | 4 | 3.825 | 4.175 | TP53 |
| Age | 0.0001056 | 0.0000509 | 0.95 | 0.0000059 | 0.0002053 | 2.0755326 | 123 | 0.0379372 | * | 0.00 * | pos | 1 | 0.825 | 1.175 | TP53 |
| UntreatedVSTrilaciclib | 0.0091865 | 0.0033285 | 0.95 | 0.0026628 | 0.0157102 | 2.7599497 | 171 | 0.0057810 | ** | 0.01 ** | pos | 3 | 2.825 | 3.175 | PPM1D |
| UntreatedVSPlacebo | 0.0149956 | 0.0033377 | 0.95 | 0.0084538 | 0.0215374 | 4.4927820 | 171 | 0.0000070 | *** | 0.01 *** | pos | 2 | 1.825 | 2.175 | PPM1D |
| Age | -0.0000390 | 0.0000578 | 0.95 | -0.0001522 | 0.0000743 | -0.6747297 | 171 | 0.4998475 | -0.00 | neg | 1 | 0.825 | 1.175 | PPM1D | |
| TrilaciclibVSPlacebo | 0.0057269 | 0.0012940 | 0.95 | 0.0031908 | 0.0082631 | 4.4258505 | 163 | 0.0000096 | *** | 0.01 *** | pos | 4 | 3.825 | 4.175 | PPM1D |
| Age | -0.0000745 | 0.0000632 | 0.95 | -0.0001983 | 0.0000493 | -1.1790850 | 163 | 0.2383643 | -0.00 | neg | 1 | 0.825 | 1.175 | PPM1D | |
| UntreatedVSTrilaciclib | 0.0050014 | 0.0031218 | 0.95 | -0.0011173 | 0.0111201 | 1.6020752 | 67 | 0.1091390 | 0.01 | pos | 3 | 2.825 | 3.175 | CHEK2 | |
| UntreatedVSPlacebo | 0.0079642 | 0.0029400 | 0.95 | 0.0022018 | 0.0137265 | 2.7088733 | 67 | 0.0067512 | ** | 0.01 ** | pos | 2 | 1.825 | 2.175 | CHEK2 |
| Age | 0.0000618 | 0.0000770 | 0.95 | -0.0000891 | 0.0002128 | 0.8030364 | 67 | 0.4219537 | 0.00 | pos | 1 | 0.825 | 1.175 | CHEK2 | |
| TrilaciclibVSPlacebo | 0.0029743 | 0.0018407 | 0.95 | -0.0006334 | 0.0065820 | 1.6158468 | 59 | 0.1061274 | 0.00 | pos | 4 | 3.825 | 4.175 | CHEK2 | |
| Age | 0.0000590 | 0.0000897 | 0.95 | -0.0001168 | 0.0002348 | 0.6576834 | 59 | 0.5107416 | 0.00 | pos | 1 | 0.825 | 1.175 | CHEK2 |
model_growth_rate_variant_class <- map_dfr(c("Missense", "Nonsense"), function(classification) {
print(classification)
model_growth_rate_variant_class <- rbind(
glm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene == "TP53") %>%
filter(Variant_Classification == classification) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = ifelse(term == "TrilaciclibTrilaciclib", "UntreatedVSTrilaciclib", "UntreatedVSPlacebo"),
classification = classification
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort,
data = df_to_plot %>%
filter(Gene == "TP53") %>%
filter(Variant_Classification == classification) %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = "TrilaciclibVSPlacebo",
classification = classification
)
)
model_growth_rate_variant_class
})## [1] "Missense"
## [1] "Nonsense"model_growth_rate_variant_class| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | classification |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UntreatedVSTrilaciclib | 0.0033104 | 0.0019768 | 0.95 | -0.0005639 | 0.0071848 | 1.6746765 | 117 | 0.0939977 | 0.00 | pos | 2 | 1.825 | 2.175 | Missense | |
| UntreatedVSPlacebo | 0.0059889 | 0.0019876 | 0.95 | 0.0020932 | 0.0098846 | 3.0130927 | 117 | 0.0025860 | ** | 0.01 ** | pos | 1 | 0.825 | 1.175 | Missense |
| TrilaciclibVSPlacebo | 0.0025881 | 0.0013431 | 0.95 | -0.0000442 | 0.0052204 | 1.9270258 | 103 | 0.0539764 | 0.00 | pos | 3 | 2.825 | 3.175 | Missense | |
| UntreatedVSTrilaciclib | -0.0112287 | 0.0071922 | 0.95 | -0.0253252 | 0.0028679 | -1.5612160 | 19 | 0.1184728 | -0.01 | neg | 2 | 1.825 | 2.175 | Nonsense | |
| UntreatedVSPlacebo | -0.0092531 | 0.0071018 | 0.95 | -0.0231723 | 0.0046661 | -1.3029301 | 19 | 0.1925986 | -0.01 | neg | 1 | 0.825 | 1.175 | Nonsense | |
| TrilaciclibVSPlacebo | 0.0018658 | 0.0033842 | 0.95 | -0.0047671 | 0.0084988 | 0.5513315 | 17 | 0.5814065 | 0.00 | pos | 3 | 2.825 | 3.175 | Nonsense |
model_growth_rate_gene_cohort <- map_dfr(c("TP53", "PPM1D", "CHEK2"), function(gene) {
map_dfr(c("SCLC", "TNBC", "CRC"), function(cancer) {
print(paste(gene, cancer))
rbind(
glm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene == gene & Cohort == cancer) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(
term = ifelse(term == "TrilaciclibTrilaciclib",
"UntreatedVSTrilaciclib",
"UntreatedVSPlacebo"
)
),
glm(
formula = growth_rate ~ Trilaciclib,
data = df_to_plot %>%
filter(Gene == gene & Cohort == cancer) %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(
term = "TrilaciclibVSPlacebo"
)
) %>%
mutate(
Gene = gene,
Cohort = cancer
)
})
})## [1] "TP53 SCLC"
## [1] "TP53 TNBC"
## [1] "TP53 CRC"
## [1] "PPM1D SCLC"
## [1] "PPM1D TNBC"
## [1] "PPM1D CRC"
## [1] "CHEK2 SCLC"
## [1] "CHEK2 TNBC"
## [1] "CHEK2 CRC"df_to_plot %>% filter(growth_rate != "Inf" & growth_rate != "-Inf") %>% dplyr::select(Gene, Trilaciclib) %>% table()## Trilaciclib
## Gene Untreated Trilaciclib Placebo
## TP53 14 64 64
## PPM1D 7 85 83
## CHEK2 7 29 35
## DNMT3A 81 202 222
## TET2 42 58 99
## ASXL1 10 16 24
## SF3B1 1 5 9
## SRSF2 2 1 5
## JAK2 0 3 4counts_genes <- df_to_plot %>%
filter(growth_rate != Inf & growth_rate != -Inf) %>%
filter(Gene_Class == "DDR") %>%
group_by(Trilaciclib, Gene) %>%
summarise(n = n(), .groups = "drop")
counts_variant_class <- df_to_plot %>%
filter(growth_rate != Inf & growth_rate != -Inf) %>%
filter(Gene == "TP53") %>%
group_by(Trilaciclib, Variant_Classification) %>%
summarise(n = n(), .groups = "drop")
fig2e <- df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene_Class == "DDR" ) %>%
ggplot(
aes(
x = Gene,
y = growth_rate,
fill = Trilaciclib
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
geom_text(data = counts_genes, aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = ""
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 5, 0.75, 1, model_growth_rate_gene$p.value[1], model_growth_rate_gene$p.stars[1], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 6, 0.75, 1.25, model_growth_rate_gene$p.value[2], model_growth_rate_gene$p.stars[2], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 7, 1, 1.25, model_growth_rate_gene$p.value[3], model_growth_rate_gene$p.stars[3], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D"), 5, 1.75, 2, model_growth_rate_gene$p.value[4], model_growth_rate_gene$p.stars[4], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D"), 6, 1.75, 2.25, model_growth_rate_gene$p.value[5], model_growth_rate_gene$p.stars[5], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D"), 7.1, 2, 2.25, model_growth_rate_gene$p.value[6], model_growth_rate_gene$p.stars[6], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2"), 5, 2.75, 3, model_growth_rate_gene$p.value[7], model_growth_rate_gene$p.stars[7], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2"), 6, 2.75, 3.25, model_growth_rate_gene$p.value[8], model_growth_rate_gene$p.stars[8], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2"), 7.1, 3, 3.25, model_growth_rate_gene$p.value[9], model_growth_rate_gene$p.stars[9], show_values = FALSE, hide_ns = FALSE)) ## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill` # do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "DNMT3A"), 5, 3.75, 4, model_growth_rate_gene$p.value[10], model_growth_rate_gene$p.stars[10], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "DNMT3A"), 6, 3.75, 4.25, model_growth_rate_gene$p.value[11], model_growth_rate_gene$p.stars[11], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "DNMT3A"), 7, 4, 4.25, model_growth_rate_gene$p.value[12], model_growth_rate_gene$p.stars[12], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TET2"), 5, 4.75, 5, model_growth_rate_gene$p.value[13], model_growth_rate_gene$p.stars[13], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TET2"), 6, 4.75, 5.25, model_growth_rate_gene$p.value[14], model_growth_rate_gene$p.stars[14], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TET2"), 7, 5, 5.25, model_growth_rate_gene$p.value[15], model_growth_rate_gene$p.stars[15], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "ASXL1"), 5, 5.75, 6, model_growth_rate_gene$p.value[16], model_growth_rate_gene$p.stars[16], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "ASXL1"), 6, 5.75, 6.25, model_growth_rate_gene$p.value[17], model_growth_rate_gene$p.stars[17], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "ASXL1"), 7, 6, 6.25, model_growth_rate_gene$p.value[18], model_growth_rate_gene$p.stars[18], show_values = FALSE, hide_ns = FALSE))
ext_fig_tp53_missense <- df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene == "TP53") %>%
ggplot(
aes(
x = Variant_Classification,
y = growth_rate,
fill = Trilaciclib
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
geom_text(data = counts_variant_class, aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = "TP53"
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 5, 0.75, 1, model_growth_rate_variant_class$p.value[1], model_growth_rate_variant_class$p.stars[1], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 6, 0.75, 1.25, model_growth_rate_variant_class$p.value[2], model_growth_rate_variant_class$p.stars[2], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 7, 1, 1.25, model_growth_rate_variant_class$p.value[3], model_growth_rate_variant_class$p.stars[3], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 5, 1.75, 2, model_growth_rate_variant_class$p.value[4], model_growth_rate_variant_class$p.stars[4], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 6, 1.75, 2.25, model_growth_rate_variant_class$p.value[5], model_growth_rate_variant_class$p.stars[5], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 7, 2, 2.25, model_growth_rate_variant_class$p.value[6], model_growth_rate_variant_class$p.stars[6], show_values = FALSE, hide_ns = FALSE))## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`ext_fig_tp53_nonsense <- df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene == "TP53", Variant_Classification == "Nonsense") %>%
ggplot(
aes(
x = "Nonsense",
y = growth_rate,
fill = Trilaciclib
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
geom_text(data = counts_variant_class %>% filter(Variant_Classification == "Nonsense"), aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = "TP53"
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = treatment_colors) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 5, 0.75, 1, model_growth_rate_variant_class$p.value[4], model_growth_rate_variant_class$p.stars[4], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 6, 0.75, 1.25, model_growth_rate_variant_class$p.value[5], model_growth_rate_variant_class$p.stars[5], show_values = FALSE, hide_ns = FALSE)) +
do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53"), 7, 1, 1.25, model_growth_rate_variant_class$p.value[6], model_growth_rate_variant_class$p.stars[6], show_values = FALSE, hide_ns = FALSE))## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`interaction_colors <- c(
"Untreated.Untreated" = "#BC3C29FF",
"Placebo.SCLC" = "#F4BC8C",
"Placebo.TNBC" = "#E18727FF",
"Placebo.CRC" = "#A66421FF",
"Trilaciclib.SCLC" = "#7ABD91",
"Trilaciclib.TNBC" = "#20854EFF",
"Trilaciclib.CRC" = "#166339FF"
)
df_to_plot %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene_Class == "DDR" ) %>%
mutate(group = interaction(Trilaciclib, Cohort)) %>%
ggplot(
aes(
x = Gene,
y = growth_rate,
fill = group
)
) +
geom_hline(yintercept = 0, color = "black", alpha = 0.3) +
geom_boxplot(outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=0.2), alpha = 0.3) +
#geom_text(data = counts_genes, aes(label = n, y = 0.06), size = 8, position = position_dodge(width = 0.8)) +
labs(
y = "Growth Rate",
x = '',
title = ""
) +
panel_theme_basic +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), breaks = c(-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.05), limit = c(NA, 1.5)) +
scale_fill_manual(values = interaction_colors)
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53", Cohort == "SCLC"), 5, 0.75, 1, model_growth_rate_gene_cohort$p.value[1], model_growth_rate_gene_cohort$p.stars[1], show_values = FALSE, hide_ns = FALSE))
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53", Cohort == "SCLC"), 6, 0.75, 1.25, model_growth_rate_gene_cohort$p.value[2], model_growth_rate_gene_cohort$p.stars[2], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53", Cohort == "TNBC"), 7, 1, 1.25, model_growth_rate_gene_cohort$p.value[3], model_growth_rate_gene_cohort$p.stars[3], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53", Cohort == "TNBC"), 5, 1.75, 2, model_growth_rate_gene_cohort$p.value[4], model_growth_rate_gene_cohort$p.stars[4], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53", Cohort == "CRC"), 6, 1.75, 2.25, model_growth_rate_gene_cohort$p.value[5], model_growth_rate_gene_cohort$p.stars[5], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "TP53", Cohort == "CRC"), 7.1, 2, 2.25, model_growth_rate_gene_cohort$p.value[6], model_growth_rate_gene_cohort$p.stars[6], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D", Cohort == "SCLC"), 5, 2.75, 3, model_growth_rate_gene_cohort$p.value[7], model_growth_rate_gene_cohort$p.stars[7], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D", Cohort == "SCLC"), 6, 2.75, 3.25, model_growth_rate_gene_cohort$p.value[8], model_growth_rate_gene_cohort$p.stars[8], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D", Cohort == "TNBC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[9], model_growth_rate_gene_cohort$p.stars[9], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D", Cohort == "TNBC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[10], model_growth_rate_gene_cohort$p.stars[10], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D", Cohort == "CRC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[11], model_growth_rate_gene_cohort$p.stars[11], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "PPM1D", Cohort == "CRC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[12], model_growth_rate_gene_cohort$p.stars[12], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2", Cohort == "SCLC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[13], model_growth_rate_gene_cohort$p.stars[13], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2", Cohort == "SCLC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[14], model_growth_rate_gene_cohort$p.stars[14], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2", Cohort == "TNBC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[15], model_growth_rate_gene_cohort$p.stars[15], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2", Cohort == "TNBC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[16], model_growth_rate_gene_cohort$p.stars[16], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2", Cohort == "CRC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[17], model_growth_rate_gene_cohort$p.stars[17], show_values = FALSE, hide_ns = FALSE)) +
# do.call(geom_signif, default_sig_data(df_to_plot %>% filter(Gene == "CHEK2", Cohort == "CRC"), 7.1, 3, 3.25, model_growth_rate_gene_cohort$p.value[18], model_growth_rate_gene_cohort$p.stars[18], show_values = FALSE, hide_ns = FALSE))
fig2e## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_signif()`).## Warning: Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
ggsave(paste0(figures_save_path, "Figure2E.png"), plot = fig2e, width = 20, height = 8, dpi = 300)## Warning: Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).
## Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).18 Extended Figure
#source("utils/custom_lollipop.R")
# colors.mutation_type <- brewer.pal(8,"Dark2")[1:8]
# names(colors.mutation_type) <- c("frameshift_variant", "inframe_insertion", "splice_region_variant", "inframe_deletion", 'start_lost','stop_gained','missense_variant', "synonymous_variant")
# # g1_tp %>%
# # filter(Cohort != "Untreated", Gene == "TP53") %>%
# # mutate(
# # ID = DeID,
# # PROTEIN_CHANGE = gene_aachange,
# # PROTEIN_POS = as.numeric(sub(".*?([0-9]+).*", "\\1", sub(".*_", "", gene_aachange))),
# # hit = case_when(
# # Trilaciclib == "Placebo" ~ 1,
# # Trilaciclib == "Trilaciclib" ~ 2
# # ),
# # gene = "TP53",
# # mutation_type = VariantClass
# # ) %>%
# # dplyr::select(PROTEIN_POS) %>%
# # filter(PROTEIN_POS >= 95 & PROTEIN_POS <= 288)
# prot.info = get_protein_info("TP53")
# ext_fig_tp53_lollipop <- g1_tp %>%
# filter(Cohort != "Untreated", Gene == "TP53") %>%
# mutate(
# ID = DeID,
# PROTEIN_CHANGE = gene_aachange,
# PROTEIN_POS = as.numeric(sub(".*?([0-9]+).*", "\\1", sub(".*_", "", gene_aachange))),
# hit = case_when(
# Trilaciclib == "Placebo" ~ 1,
# Trilaciclib == "Trilaciclib" ~ 2
# ),
# gene = "TP53",
# mutation_type = VariantClass
# ) %>%
# my_lollipop(dat.genetics.unique=.,
# current.gene="TP53",
# protein_domain=prot.info$protein_domain,
# protein_length=prot.info$protein_length,
# cutoff.hotspot = 20,
# colors.mutation_type=colors.mutation_type)
# ggsave(paste0(figures_save_path, "ExtendedFigure1A.png"), plot = ext_fig_tp53_lollipop, width = 12, height = 8, dpi = 300)
# prot.info = get_protein_info("PPM1D")
# ext_fig_ppm1d_lollipop <- g1_tp %>%
# filter(Cohort != "Untreated", Gene == "PPM1D") %>%
# mutate(
# ID = DeID,
# PROTEIN_CHANGE = gene_aachange,
# PROTEIN_POS = as.numeric(sub(".*?([0-9]+).*", "\\1", sub(".*_", "", gene_aachange))),
# hit = case_when(
# Trilaciclib == "Placebo" ~ 1,
# Trilaciclib == "Trilaciclib" ~ 2
# ),
# gene = "PPM1D",
# mutation_type = VariantClass
# ) %>%
# my_lollipop(dat.genetics.unique=.,
# current.gene="PPM1D",
# protein_domain=prot.info$protein_domain,
# protein_length=prot.info$protein_length,
# cutoff.hotspot = 20,
# colors.mutation_type=colors.mutation_type)
# ggsave(paste0(figures_save_path, "ExtendedFigure1B.png"), plot = ext_fig_ppm1d_lollipop, width = 12, height = 8, dpi = 300)
# prot.info = get_protein_info("CHEK2")
# ext_fig_chek2_lollipop <- g1_tp %>%
# filter(Cohort != "Untreated", Gene == "CHEK2") %>%
# mutate(
# ID = DeID,
# PROTEIN_CHANGE = gene_aachange,
# PROTEIN_POS = as.numeric(sub(".*?([0-9]+).*", "\\1", sub(".*_", "", gene_aachange))),
# hit = case_when(
# Trilaciclib == "Placebo" ~ 1,
# Trilaciclib == "Trilaciclib" ~ 2
# ),
# gene = "CHEK2",
# mutation_type = VariantClass
# ) %>%
# my_lollipop(dat.genetics.unique=.,
# current.gene="CHEK2",
# protein_domain=prot.info$protein_domain,
# protein_length=prot.info$protein_length,
# cutoff.hotspot = 20,
# colors.mutation_type=colors.mutation_type)
# ggsave(paste0(figures_save_path, "ExtendedFigure1C.png"), plot = ext_fig_chek2_lollipop, width = 12, height = 8, dpi = 300)19 Extended Figure 1 Bubble Plot
df_to_plot_ddr <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
),
Gene %in% DDR_genes,
Cohort != "Untreated"
) %>%
mutate(
VariantClass = case_when(
VariantClass == "missense_variant" ~ "Missense",
VariantClass == "frameshift_variant" ~ "Frameshift",
VariantClass == "inframe_insertion" ~ "Missense",
VariantClass == "inframe_deletion" ~ "Missense",
VariantClass == "splice_region_variant" ~ "Splicing",
VariantClass == "start_lost" ~ "Nonsense",
VariantClass == "stop_gained" ~ "Nonsense",
VariantClass == "synonymous_variant" ~ "Synonymous",
TRUE ~ "Other"
),
VariantClass = factor(VariantClass, levels = c("Missense", "Frameshift", "Splicing", "Nonsense", "Synonymous", "Other"))
)
ext_fig_tp53_bubble <- df_to_plot_ddr %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene == "TP53") %>%
mutate(
# size_bin = case_when(
# CosmicCount < 10 ~ "0-10",
# CosmicCount >= 10 & CosmicCount < 50 ~ "10-50",
# CosmicCount >= 50 & CosmicCount < 100 ~ "50-100",
# CosmicCount >= 100 ~ ">100"
# ),
size_bin = case_when(
heme_cosmic_count < 10 ~ "0-10",
heme_cosmic_count >= 10 & heme_cosmic_count < 25 ~ "10-25",
heme_cosmic_count >= 25 & heme_cosmic_count < 50 ~ "25-50",
heme_cosmic_count >= 50 ~ ">50"
),
#size_bin = factor(size_bin, levels = c("0-10", "10-50", "50-100", ">100")),
size_bin = factor(size_bin, levels = c("0-10", "10-25", "25-50", ">50"))
) %>%
ggplot(
aes(
x = preVAF,
y = growth_rate,
color = Cohort,
size = size_bin,
shape = VariantClass
)
) +
geom_point(alpha = 0.7) +
#scale_size_manual(values = c("0-10"= 6, "10-50"=8, "50-100"= 10, ">100"= 12)) +
scale_size_manual(values = c("0-10"= 6, "10-25"=8, "25-50"= 10, ">50"= 12)) +
scale_shape_manual(values = c("Missense" = 16, "Frameshift" = 17, "Splicing" = 18, "Nonsense" = 15, "Synonymous" = 1, "Other" = 2)) +
panel_theme_basic +
labs(
x = "VAF Pre-Treatment",
y = "Growth Rate",
title = "TP53",
color = "Clinical Trial:",
size = "Heme Cosmic Count:",
shape = "Mutation Type:"
) +
scale_fill_nejm() +
scale_color_nejm() +
scale_x_continuous(trans = scales::pseudo_log_trans(0.001), limit = c(NA, NA), breaks = c(0, 0.01, 0.05, 0.01, 0.05, 0.1)) +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), limit = c(NA, 0.05)) +
theme(
legend.position = c(0.60, 0.95),
legend.box = "verticle",
legend.title = element_text(size = 18),
legend.text = element_text(size = 18),
legend.spacing = unit(0.01, "cm"),
plot.margin = margin(1, 1, 1, 1, "cm"),
) +
guides(
color = guide_legend(byrow = TRUE, order = 1, override.aes = list(size = 5)),
shape = guide_legend(byrow = TRUE, order = 2, override.aes = list(size = 5)),
size = guide_legend(byrow = TRUE, order = 3, override.aes = list(size = 5))
)
ggsave(paste0(figures_save_path, "ExtendedFigure1D.png"), plot = ext_fig_tp53_bubble, width = 10, height = 7, dpi = 300)
ext_fig_ppm1d_bubble <- df_to_plot_ddr %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene == "PPM1D") %>%
mutate(
# size_bin = case_when(
# CosmicCount < 10 ~ "0-10",
# CosmicCount >= 10 & CosmicCount < 50 ~ "10-50",
# CosmicCount >= 50 & CosmicCount < 100 ~ "50-100",
# CosmicCount >= 100 ~ ">100"
# ),
size_bin = case_when(
heme_cosmic_count < 10 ~ "0-10",
heme_cosmic_count >= 10 & heme_cosmic_count < 25 ~ "10-25",
heme_cosmic_count >= 25 & heme_cosmic_count < 50 ~ "25-50",
heme_cosmic_count >= 50 ~ ">50"
),
#size_bin = factor(size_bin, levels = c("0-10", "10-50", "50-100", ">100")),
size_bin = factor(size_bin, levels = c("0-10", "10-25", "25-50", ">50"))
) %>%
ggplot(
aes(
x = preVAF,
y = growth_rate,
color = Cohort,
size = size_bin,
shape = VariantClass
)
) +
geom_point(alpha = 0.7) +
#scale_size_manual(values = c("0-10"= 6, "10-50"=8, "50-100"= 10, ">100"= 12)) +
scale_size_manual(values = c("0-10"= 6, "10-25"=8, "25-50"= 10, ">50"= 12)) +
scale_shape_manual(values = c("Missense" = 16, "Frameshift" = 17, "Splicing" = 18, "Nonsense" = 15, "Synonymous" = 1, "Other" = 2)) +
panel_theme_basic +
labs(
x = "VAF Pre-Treatment",
y = "Growth Rate",
title = "PPM1D",
color = "Clinical Trial:",
size = "Heme Cosmic Count:",
shape = "Mutation Type:"
) +
scale_fill_nejm() +
scale_color_nejm() +
scale_x_continuous(trans = scales::pseudo_log_trans(0.001), limit = c(NA, NA), breaks = c(0, 0.01, 0.05, 0.01, 0.05, 0.1)) +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), limit = c(NA, 0.05)) +
theme(
legend.position = "none",
plot.margin = margin(1, 1, 1, 1, "cm"),
)
ggsave(paste0(figures_save_path, "ExtendedFigure1E.png"), plot = ext_fig_ppm1d_bubble, width = 10, height = 7, dpi = 300)
ext_fig_chek2_bubble <- df_to_plot_ddr %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Gene == "CHEK2") %>%
mutate(
# size_bin = case_when(
# CosmicCount < 10 ~ "0-10",
# CosmicCount >= 10 & CosmicCount < 50 ~ "10-50",
# CosmicCount >= 50 & CosmicCount < 100 ~ "50-100",
# CosmicCount >= 100 ~ ">100"
# ),
size_bin = case_when(
heme_cosmic_count < 10 ~ "0-10",
heme_cosmic_count >= 10 & heme_cosmic_count < 25 ~ "10-25",
heme_cosmic_count >= 25 & heme_cosmic_count < 50 ~ "25-50",
heme_cosmic_count >= 50 ~ ">50"
),
#size_bin = factor(size_bin, levels = c("0-10", "10-50", "50-100", ">100")),
size_bin = factor(size_bin, levels = c("0-10", "10-25", "25-50", ">50"))
) %>%
ggplot(
aes(
x = preVAF,
y = growth_rate,
color = Cohort,
size = size_bin,
shape = VariantClass
)
) +
geom_point(alpha = 0.7) +
#scale_size_manual(values = c("0-10"= 6, "10-50"=8, "50-100"= 10, ">100"= 12)) +
scale_size_manual(values = c("0-10"= 6, "10-25"=8, "25-50"= 10, ">50"= 12)) +
scale_shape_manual(values = c("Missense" = 16, "Frameshift" = 17, "Splicing" = 18, "Nonsense" = 15, "Synonymous" = 1, "Other" = 2)) +
panel_theme_basic +
labs(
x = "VAF Pre-Treatment",
y = "Growth Rate",
title = "CHEK2",
color = "Clinical Trial:",
size = "Heme Cosmic Count:",
shape = "Mutation Type:"
) +
scale_fill_nejm() +
scale_color_nejm() +
scale_x_continuous(trans = scales::pseudo_log_trans(0.001), limit = c(NA, NA), breaks = c(0, 0.01, 0.05, 0.01, 0.05, 0.1)) +
scale_y_continuous(trans = scales::pseudo_log_trans(0.001), limit = c(NA, 0.05)) +
theme(
legend.position = "none",
plot.margin = margin(1, 1, 1, 1, "cm"),
)
ggsave(paste0(figures_save_path, "ExtendedFigure1F.png"), plot = ext_fig_chek2_bubble, width = 10, height = 7, dpi = 300)
ggsave(paste0(figures_save_path, "ExtendedFigure1G.png"), plot = ext_fig_tp53_missense, width = 10, height = 7, dpi = 300)20 Sensitivity Analysis - Combined
df_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
)
model_growth_rate_gene <- map_dfr(c("TP53", "PPM1D", "CHEK2"), function(gene) {
print(gene)
model_growth_rate_gene <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene == gene) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
gene = gene
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene == gene) %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
gene = gene
)
)
model_growth_rate_gene
})## [1] "TP53"
## [1] "PPM1D"
## [1] "CHEK2"model_growth_rate_gene| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | gene |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UntreatedVSTrilaciclib | 0.0034097 | 0.0019805 | 0.95 | -0.0004720 | 0.0072914 | 1.7216448 | 136 | 0.0851339 | 0.00 | pos | 5 | 4.825 | 5.175 | TP53 | |
| UntreatedVSPlacebo | 0.0059762 | 0.0019604 | 0.95 | 0.0021338 | 0.0098185 | 3.0484061 | 136 | 0.0023006 | ** | 0.01 ** | pos | 4 | 3.825 | 4.175 | TP53 |
| Age | 0.0001012 | 0.0000451 | 0.95 | 0.0000127 | 0.0001897 | 2.2420769 | 136 | 0.0249564 | * | 0.00 * | pos | 3 | 2.825 | 3.175 | TP53 |
| SexFemale | 0.0009292 | 0.0011052 | 0.95 | -0.0012369 | 0.0030954 | 0.8408001 | 136 | 0.4004599 | 0.00 | pos | 2 | 1.825 | 2.175 | TP53 | |
| RaceNon-White | 0.0006894 | 0.0015510 | 0.95 | -0.0023505 | 0.0037293 | 0.4445030 | 136 | 0.6566790 | 0.00 | pos | 1 | 0.825 | 1.175 | TP53 | |
| TrilaciclibVSPlacebo | 0.0024756 | 0.0012320 | 0.95 | 0.0000609 | 0.0048903 | 2.0094296 | 121 | 0.0444916 | * | 0.00 * | pos | 6 | 5.825 | 6.175 | TP53 |
| Age | 0.0001129 | 0.0000520 | 0.95 | 0.0000109 | 0.0002148 | 2.1695095 | 121 | 0.0300440 | * | 0.00 * | pos | 3 | 2.825 | 3.175 | TP53 |
| SexFemale | -0.0002078 | 0.0016469 | 0.95 | -0.0034358 | 0.0030201 | -0.1261793 | 121 | 0.8995900 | -0.00 | neg | 2 | 1.825 | 2.175 | TP53 | |
| RaceNon-White | 0.0016137 | 0.0016989 | 0.95 | -0.0017161 | 0.0049436 | 0.9498649 | 121 | 0.3421809 | 0.00 | pos | 1 | 0.825 | 1.175 | TP53 | |
| UntreatedVSTrilaciclib | 0.0086118 | 0.0033611 | 0.95 | 0.0020242 | 0.0151994 | 2.5622008 | 169 | 0.0104011 | * | 0.01 * | pos | 5 | 4.825 | 5.175 | PPM1D |
| UntreatedVSPlacebo | 0.0146038 | 0.0033470 | 0.95 | 0.0080438 | 0.0211637 | 4.3632588 | 169 | 0.0000128 | *** | 0.01 *** | pos | 4 | 3.825 | 4.175 | PPM1D |
| Age | -0.0000389 | 0.0000588 | 0.95 | -0.0001542 | 0.0000763 | -0.6619998 | 169 | 0.5079713 | -0.00 | neg | 3 | 2.825 | 3.175 | PPM1D | |
| SexFemale | 0.0017895 | 0.0012853 | 0.95 | -0.0007297 | 0.0043088 | 1.3922680 | 169 | 0.1638412 | 0.00 | pos | 2 | 1.825 | 2.175 | PPM1D | |
| RaceNon-White | -0.0009448 | 0.0017225 | 0.95 | -0.0043209 | 0.0024313 | -0.5485064 | 169 | 0.5833442 | -0.00 | neg | 1 | 0.825 | 1.175 | PPM1D | |
| TrilaciclibVSPlacebo | 0.0058463 | 0.0013352 | 0.95 | 0.0032294 | 0.0084631 | 4.3787136 | 161 | 0.0000119 | *** | 0.01 *** | pos | 6 | 5.825 | 6.175 | PPM1D |
| Age | -0.0000812 | 0.0000646 | 0.95 | -0.0002077 | 0.0000453 | -1.2575795 | 161 | 0.2085439 | -0.00 | neg | 3 | 2.825 | 3.175 | PPM1D | |
| SexFemale | -0.0008648 | 0.0018065 | 0.95 | -0.0044054 | 0.0026757 | -0.4787521 | 161 | 0.6321150 | -0.00 | neg | 2 | 1.825 | 2.175 | PPM1D | |
| RaceNon-White | -0.0007589 | 0.0018075 | 0.95 | -0.0043015 | 0.0027837 | -0.4198567 | 161 | 0.6745901 | -0.00 | neg | 1 | 0.825 | 1.175 | PPM1D | |
| UntreatedVSTrilaciclib | 0.0051875 | 0.0031902 | 0.95 | -0.0010651 | 0.0114402 | 1.6260890 | 65 | 0.1039307 | 0.01 | pos | 5 | 4.825 | 5.175 | CHEK2 | |
| UntreatedVSPlacebo | 0.0079558 | 0.0029887 | 0.95 | 0.0020981 | 0.0138134 | 2.6619786 | 65 | 0.0077683 | ** | 0.01 ** | pos | 4 | 3.825 | 4.175 | CHEK2 |
| Age | 0.0000633 | 0.0000804 | 0.95 | -0.0000943 | 0.0002209 | 0.7868569 | 65 | 0.4313657 | 0.00 | pos | 3 | 2.825 | 3.175 | CHEK2 | |
| SexFemale | -0.0007431 | 0.0016833 | 0.95 | -0.0040423 | 0.0025561 | -0.4414571 | 65 | 0.6588821 | -0.00 | neg | 2 | 1.825 | 2.175 | CHEK2 | |
| RaceNon-White | 0.0005244 | 0.0027095 | 0.95 | -0.0047861 | 0.0058350 | 0.1935500 | 65 | 0.8465283 | 0.00 | pos | 1 | 0.825 | 1.175 | CHEK2 | |
| TrilaciclibVSPlacebo | 0.0028331 | 0.0019012 | 0.95 | -0.0008931 | 0.0065593 | 1.4901985 | 57 | 0.1361720 | 0.00 | pos | 6 | 5.825 | 6.175 | CHEK2 | |
| Age | 0.0000619 | 0.0000935 | 0.95 | -0.0001213 | 0.0002451 | 0.6626867 | 57 | 0.5075312 | 0.00 | pos | 3 | 2.825 | 3.175 | CHEK2 | |
| SexFemale | -0.0012177 | 0.0026711 | 0.95 | -0.0064530 | 0.0040176 | -0.4558627 | 57 | 0.6484887 | -0.00 | neg | 2 | 1.825 | 2.175 | CHEK2 | |
| RaceNon-White | 0.0013095 | 0.0028785 | 0.95 | -0.0043323 | 0.0069513 | 0.4549289 | 57 | 0.6491604 | 0.00 | pos | 1 | 0.825 | 1.175 | CHEK2 |
supp_table_4 <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)
# Supp Table 4
supp_table_4| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | Gene_Class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UntreatedVSTrilaciclib | -0.0004193 | 0.0008560 | 0.95 | -0.0020970 | 0.0012584 | -0.4898279 | 748 | 0.6242557 | -0.00 | neg | 5 | 4.825 | 5.175 | DTA | |
| UntreatedVSPlacebo | 0.0004505 | 0.0008192 | 0.95 | -0.0011551 | 0.0020560 | 0.5498981 | 748 | 0.5823893 | 0.00 | pos | 4 | 3.825 | 4.175 | DTA | |
| Age | 0.0000097 | 0.0000282 | 0.95 | -0.0000456 | 0.0000650 | 0.3437133 | 748 | 0.7310619 | 0.00 | pos | 3 | 2.825 | 3.175 | DTA | |
| SexFemale | -0.0010127 | 0.0005726 | 0.95 | -0.0021350 | 0.0001097 | -1.7684285 | 748 | 0.0769893 | -0.00 | neg | 2 | 1.825 | 2.175 | DTA | |
| RaceNon-White | -0.0005097 | 0.0007889 | 0.95 | -0.0020560 | 0.0010366 | -0.6460652 | 748 | 0.5182371 | -0.00 | neg | 1 | 0.825 | 1.175 | DTA | |
| TrilaciclibVSPlacebo | 0.0009428 | 0.0006610 | 0.95 | -0.0003526 | 0.0022383 | 1.4264390 | 614 | 0.1537417 | 0.00 | pos | 6 | 5.825 | 6.175 | DTA | |
| Age | 0.0000076 | 0.0000325 | 0.95 | -0.0000561 | 0.0000714 | 0.2347762 | 614 | 0.8143824 | 0.00 | pos | 3 | 2.825 | 3.175 | DTA | |
| SexFemale | -0.0012016 | 0.0007347 | 0.95 | -0.0026416 | 0.0002384 | -1.6355022 | 614 | 0.1019438 | -0.00 | neg | 2 | 1.825 | 2.175 | DTA | |
| RaceNon-White | -0.0009631 | 0.0008755 | 0.95 | -0.0026790 | 0.0007528 | -1.1000411 | 614 | 0.2713142 | -0.00 | neg | 1 | 0.825 | 1.175 | DTA | |
| UntreatedVSTrilaciclib | 0.0061118 | 0.0016932 | 0.95 | 0.0027932 | 0.0094303 | 3.6096670 | 382 | 0.0003066 | *** | 0.01 *** | pos | 5 | 4.825 | 5.175 | DDR |
| UntreatedVSPlacebo | 0.0101728 | 0.0016769 | 0.95 | 0.0068861 | 0.0134595 | 6.0663505 | 382 | 0.0000000 | *** | 0.01 *** | pos | 4 | 3.825 | 4.175 | DDR |
| Age | 0.0000565 | 0.0000363 | 0.95 | -0.0000147 | 0.0001276 | 1.5557795 | 382 | 0.1197605 | 0.00 | pos | 3 | 2.825 | 3.175 | DDR | |
| SexFemale | 0.0009388 | 0.0008254 | 0.95 | -0.0006790 | 0.0025567 | 1.1373626 | 382 | 0.2553867 | 0.00 | pos | 2 | 1.825 | 2.175 | DDR | |
| RaceNon-White | 0.0006464 | 0.0011606 | 0.95 | -0.0016283 | 0.0029211 | 0.5569530 | 382 | 0.5775595 | 0.00 | pos | 1 | 0.825 | 1.175 | DDR | |
| TrilaciclibVSPlacebo | 0.0038896 | 0.0008921 | 0.95 | 0.0021411 | 0.0056380 | 4.3600833 | 353 | 0.0000130 | *** | 0.00 *** | pos | 6 | 5.825 | 6.175 | DDR |
| Age | 0.0000419 | 0.0000408 | 0.95 | -0.0000381 | 0.0001220 | 1.0261655 | 353 | 0.3048136 | 0.00 | pos | 3 | 2.825 | 3.175 | DDR | |
| SexFemale | -0.0003616 | 0.0012065 | 0.95 | -0.0027264 | 0.0020031 | -0.2997444 | 353 | 0.7643721 | -0.00 | neg | 2 | 1.825 | 2.175 | DDR | |
| RaceNon-White | 0.0013765 | 0.0012454 | 0.95 | -0.0010644 | 0.0038174 | 1.1052767 | 353 | 0.2690397 | 0.00 | pos | 1 | 0.825 | 1.175 | DDR |
supp_table_5 <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + SmokingStatus,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("Untreated", "SCLC", "TNBC"))) %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort + Age + Sex + Race + SmokingStatus,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("SCLC", "TNBC"))) %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + SmokingStatus,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("Untreated", "SCLC", "TNBC"))) %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Cohort + Age + Sex + Race + SmokingStatus,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("SCLC", "TNBC"))) %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
filter(!grepl("Cohort", term)) %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)
# Supp Table 5
supp_table_5| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | Gene_Class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UntreatedVSTrilaciclib | -0.0012785 | 0.0012805 | 0.95 | -0.0037883 | 0.0012312 | -0.9984700 | 331 | 0.3180515 | -0.00 | neg | 8 | 7.825 | 8.175 | DTA | |
| UntreatedVSPlacebo | -0.0007904 | 0.0012115 | 0.95 | -0.0031649 | 0.0015842 | -0.6523508 | 331 | 0.5141749 | -0.00 | neg | 7 | 6.825 | 7.175 | DTA | |
| Age | 0.0000070 | 0.0000455 | 0.95 | -0.0000821 | 0.0000961 | 0.1544334 | 331 | 0.8772680 | 0.00 | pos | 6 | 5.825 | 6.175 | DTA | |
| SexFemale | -0.0001274 | 0.0009925 | 0.95 | -0.0020727 | 0.0018179 | -0.1283547 | 331 | 0.8978683 | -0.00 | neg | 5 | 4.825 | 5.175 | DTA | |
| RaceNon-White | 0.0004122 | 0.0013223 | 0.95 | -0.0021793 | 0.0030038 | 0.3117728 | 331 | 0.7552132 | 0.00 | pos | 4 | 3.825 | 4.175 | DTA | |
| SmokingStatusFormer | 0.0014399 | 0.0012494 | 0.95 | -0.0010089 | 0.0038888 | 1.1524655 | 331 | 0.2491298 | 0.00 | pos | 3 | 2.825 | 3.175 | DTA | |
| SmokingStatusCurrent | -0.0018238 | 0.0015022 | 0.95 | -0.0047681 | 0.0011204 | -1.2141062 | 331 | 0.2247072 | -0.00 | neg | 2 | 1.825 | 2.175 | DTA | |
| SmokingStatusUnknown | -0.0023494 | 0.0039967 | 0.95 | -0.0101827 | 0.0054840 | -0.5878253 | 331 | 0.5566496 | -0.00 | neg | 1 | 0.825 | 1.175 | DTA | |
| TrilaciclibVSPlacebo | 0.0010339 | 0.0012377 | 0.95 | -0.0013920 | 0.0034598 | 0.8353418 | 199 | 0.4035253 | 0.00 | pos | 7 | 6.825 | 7.175 | DTA | |
| Age | -0.0000464 | 0.0000600 | 0.95 | -0.0001640 | 0.0000713 | -0.7722100 | 199 | 0.4399901 | -0.00 | neg | 5 | 4.825 | 5.175 | DTA | |
| SexFemale | -0.0007112 | 0.0018435 | 0.95 | -0.0043243 | 0.0029019 | -0.3857822 | 199 | 0.6996580 | -0.00 | neg | 4 | 3.825 | 4.175 | DTA | |
| RaceNon-White | -0.0011829 | 0.0018792 | 0.95 | -0.0048661 | 0.0025002 | -0.6294829 | 199 | 0.5290329 | -0.00 | neg | 3 | 2.825 | 3.175 | DTA | |
| SmokingStatusFormer | 0.0013803 | 0.0017165 | 0.95 | -0.0019839 | 0.0047446 | 0.8041807 | 199 | 0.4212926 | 0.00 | pos | 2 | 1.825 | 2.175 | DTA | |
| SmokingStatusCurrent | -0.0022942 | 0.0021815 | 0.95 | -0.0065699 | 0.0019815 | -1.0516701 | 199 | 0.2929509 | -0.00 | neg | 1 | 0.825 | 1.175 | DTA | |
| UntreatedVSTrilaciclib | 0.0036098 | 0.0022159 | 0.95 | -0.0007334 | 0.0079529 | 1.6289910 | 196 | 0.1033149 | 0.00 | pos | 8 | 7.825 | 8.175 | DDR | |
| UntreatedVSPlacebo | 0.0072309 | 0.0020215 | 0.95 | 0.0032688 | 0.0111931 | 3.5769444 | 196 | 0.0003476 | *** | 0.01 *** | pos | 7 | 6.825 | 7.175 | DDR |
| Age | -0.0000546 | 0.0000509 | 0.95 | -0.0001543 | 0.0000451 | -1.0740104 | 196 | 0.2828180 | -0.00 | neg | 6 | 5.825 | 6.175 | DDR | |
| SexFemale | 0.0031637 | 0.0015352 | 0.95 | 0.0001547 | 0.0061727 | 2.0607606 | 196 | 0.0393259 | * | 0.00 * | pos | 5 | 4.825 | 5.175 | DDR |
| RaceNon-White | 0.0024734 | 0.0016373 | 0.95 | -0.0007356 | 0.0056824 | 1.5106698 | 196 | 0.1308726 | 0.00 | pos | 4 | 3.825 | 4.175 | DDR | |
| SmokingStatusFormer | 0.0027653 | 0.0013955 | 0.95 | 0.0000303 | 0.0055004 | 1.9816716 | 196 | 0.0475160 | * | 0.00 * | pos | 3 | 2.825 | 3.175 | DDR |
| SmokingStatusCurrent | 0.0067009 | 0.0019253 | 0.95 | 0.0029273 | 0.0104744 | 3.4804055 | 196 | 0.0005007 | *** | 0.01 *** | pos | 2 | 1.825 | 2.175 | DDR |
| SmokingStatusUnknown | 0.0018030 | 0.0059519 | 0.95 | -0.0098625 | 0.0134685 | 0.3029309 | 196 | 0.7619425 | 0.00 | pos | 1 | 0.825 | 1.175 | DDR | |
| TrilaciclibVSPlacebo | 0.0026274 | 0.0014808 | 0.95 | -0.0002750 | 0.0055298 | 1.7742285 | 169 | 0.0760254 | 0.00 | pos | 7 | 6.825 | 7.175 | DDR | |
| Age | -0.0000373 | 0.0000578 | 0.95 | -0.0001506 | 0.0000760 | -0.6455768 | 169 | 0.5185534 | -0.00 | neg | 5 | 4.825 | 5.175 | DDR | |
| SexFemale | 0.0056182 | 0.0020333 | 0.95 | 0.0016329 | 0.0096034 | 2.7630620 | 169 | 0.0057262 | ** | 0.01 ** | pos | 4 | 3.825 | 4.175 | DDR |
| RaceNon-White | 0.0038061 | 0.0019488 | 0.95 | -0.0000134 | 0.0076257 | 1.9530753 | 169 | 0.0508107 | 0.00 | pos | 3 | 2.825 | 3.175 | DDR | |
| SmokingStatusFormer | -0.0000341 | 0.0017543 | 0.95 | -0.0034725 | 0.0034042 | -0.0194595 | 169 | 0.9844745 | -0.00 | neg | 2 | 1.825 | 2.175 | DDR | |
| SmokingStatusCurrent | 0.0038678 | 0.0023119 | 0.95 | -0.0006635 | 0.0083990 | 1.6729869 | 169 | 0.0943299 | 0.00 | pos | 1 | 0.825 | 1.175 | DDR |
model_growth_rate_class <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("Untreated", "SCLC", "TNBC"))) %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity + SmokingStatus + change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("SCLC", "TNBC"))) %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("Untreated", "SCLC", "TNBC"))) %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity + SmokingStatus + change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN,
data = df_to_plot %>%
filter(Cohort != "CRC") %>%
mutate(Cohort = factor(Cohort, levels = c("SCLC", "TNBC"))) %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)
model_growth_rate_class <- rbind(
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity + change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN,
data = df_to_plot %>%
filter(Gene_Class == "DTA") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DTA"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibTrilaciclib" ~ "UntreatedVSTrilaciclib",
term == "TrilaciclibPlacebo" ~ "UntreatedVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
),
glm(
formula = growth_rate ~ Trilaciclib + Age + Sex + Race + Ethnicity + change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
mutate(Trilaciclib = factor(Trilaciclib, levels = c("Trilaciclib", "Placebo"))) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
TRUE ~ term
),
Gene_Class = "DDR"
)
)21 Extra TP53 Analysis
df_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
)
model_growth_rate_cosmic_count_tp53 <- glm(
formula = growth_rate ~ Trilaciclib + preVAF + CosmicCount + heme_cosmic_count + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene == "TP53") %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "CohortTNBC" ~ "SCLCvsTNBC",
term == "CohortCRC" ~ "SCLCvsCRC",
TRUE ~ term
)
)
model_growth_rate_cosmic_count_tp53| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TrilaciclibVSPlacebo | 0.0024906 | 0.0012385 | 0.95 | 0.0000632 | 0.0049180 | 2.0109850 | 118 | 0.0443270 | * | 0.00 * | pos | 9 | 8.825 | 9.175 |
| preVAF | -0.0394023 | 0.0324058 | 0.95 | -0.1029165 | 0.0241120 | -1.2159009 | 118 | 0.2240227 | -0.04 | neg | 8 | 7.825 | 8.175 | |
| CosmicCount | -0.0000002 | 0.0000036 | 0.95 | -0.0000073 | 0.0000069 | -0.0560298 | 118 | 0.9553180 | -0.00 | neg | 7 | 6.825 | 7.175 | |
| heme_cosmic_count | 0.0000319 | 0.0000706 | 0.95 | -0.0001065 | 0.0001704 | 0.4519045 | 118 | 0.6513378 | 0.00 | pos | 6 | 5.825 | 6.175 | |
| SCLCvsTNBC | -0.0012031 | 0.0021011 | 0.95 | -0.0053212 | 0.0029151 | -0.5725839 | 118 | 0.5669265 | -0.00 | neg | 5 | 4.825 | 5.175 | |
| SCLCvsCRC | -0.0028106 | 0.0018803 | 0.95 | -0.0064959 | 0.0008748 | -1.4947404 | 118 | 0.1349822 | -0.00 | neg | 4 | 3.825 | 4.175 | |
| Age | 0.0001207 | 0.0000528 | 0.95 | 0.0000173 | 0.0002242 | 2.2873112 | 118 | 0.0221777 | * | 0.00 * | pos | 3 | 2.825 | 3.175 |
| SexFemale | -0.0003225 | 0.0016540 | 0.95 | -0.0035643 | 0.0029193 | -0.1949850 | 118 | 0.8454047 | -0.00 | neg | 2 | 1.825 | 2.175 | |
| RaceNon-White | 0.0013892 | 0.0017105 | 0.95 | -0.0019632 | 0.0047416 | 0.8121886 | 118 | 0.4166834 | 0.00 | pos | 1 | 0.825 | 1.175 |
model_growth_rate_cosmic_count_ppm1d <- glm(
formula = growth_rate ~ Trilaciclib + preVAF + CosmicCount + heme_cosmic_count + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene == "PPM1D") %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "CohortTNBC" ~ "SCLCvsTNBC",
term == "CohortCRC" ~ "SCLCvsCRC",
TRUE ~ term
)
)
model_growth_rate_cosmic_count_ppm1d| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TrilaciclibVSPlacebo | 0.0057048 | 0.0013198 | 0.95 | 0.0031180 | 0.0082915 | 4.3224943 | 158 | 0.0000154 | *** | 0.01 *** | pos | 9 | 8.825 | 9.175 |
| preVAF | -0.0358074 | 0.0469521 | 0.95 | -0.1278319 | 0.0562171 | -0.7626355 | 158 | 0.4456808 | -0.04 | neg | 8 | 7.825 | 8.175 | |
| CosmicCount | -0.0000921 | 0.0002094 | 0.95 | -0.0005026 | 0.0003184 | -0.4396938 | 158 | 0.6601589 | -0.00 | neg | 7 | 6.825 | 7.175 | |
| heme_cosmic_count | -0.0006480 | 0.0005733 | 0.95 | -0.0017717 | 0.0004756 | -1.1303359 | 158 | 0.2583347 | -0.00 | neg | 6 | 5.825 | 6.175 | |
| SCLCvsTNBC | -0.0029524 | 0.0021387 | 0.95 | -0.0071442 | 0.0012395 | -1.3804302 | 158 | 0.1674542 | -0.00 | neg | 5 | 4.825 | 5.175 | |
| SCLCvsCRC | -0.0055207 | 0.0019564 | 0.95 | -0.0093551 | -0.0016862 | -2.8218682 | 158 | 0.0047745 | ** | -0.01 ** | neg | 4 | 3.825 | 4.175 |
| Age | -0.0000920 | 0.0000638 | 0.95 | -0.0002170 | 0.0000330 | -1.4428806 | 158 | 0.1490541 | -0.00 | neg | 3 | 2.825 | 3.175 | |
| SexFemale | -0.0006944 | 0.0018043 | 0.95 | -0.0042308 | 0.0028421 | -0.3848344 | 158 | 0.7003601 | -0.00 | neg | 2 | 1.825 | 2.175 | |
| RaceNon-White | -0.0009411 | 0.0017796 | 0.95 | -0.0044290 | 0.0025467 | -0.5288664 | 158 | 0.5968981 | -0.00 | neg | 1 | 0.825 | 1.175 |
model_growth_rate_cosmic_count_chek2 <- glm(
formula = growth_rate ~ Trilaciclib + preVAF + CosmicCount + heme_cosmic_count + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene == "CHEK2") %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "CohortTNBC" ~ "SCLCvsTNBC",
term == "CohortCRC" ~ "SCLCvsCRC",
TRUE ~ term
)
)
model_growth_rate_cosmic_count_chek2| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TrilaciclibVSPlacebo | 0.0029152 | 0.0019429 | 0.95 | -0.0008928 | 0.0067231 | 1.5004570 | 54 | 0.1334961 | 0.00 | pos | 9 | 8.825 | 9.175 | |
| preVAF | -0.0290738 | 0.0188689 | 0.95 | -0.0660562 | 0.0079085 | -1.5408341 | 54 | 0.1233572 | -0.03 | neg | 8 | 7.825 | 8.175 | |
| CosmicCount | -0.0001441 | 0.0007948 | 0.95 | -0.0017018 | 0.0014136 | -0.1812782 | 54 | 0.8561492 | -0.00 | neg | 7 | 6.825 | 7.175 | |
| heme_cosmic_count | -0.0024395 | 0.0059109 | 0.95 | -0.0140247 | 0.0091457 | -0.4127083 | 54 | 0.6798203 | -0.00 | neg | 6 | 5.825 | 6.175 | |
| SCLCvsTNBC | -0.0024415 | 0.0032384 | 0.95 | -0.0087886 | 0.0039055 | -0.7539356 | 54 | 0.4508879 | -0.00 | neg | 5 | 4.825 | 5.175 | |
| SCLCvsCRC | -0.0034162 | 0.0027355 | 0.95 | -0.0087777 | 0.0019453 | -1.2488384 | 54 | 0.2117242 | -0.00 | neg | 4 | 3.825 | 4.175 | |
| Age | 0.0000721 | 0.0000953 | 0.95 | -0.0001146 | 0.0002588 | 0.7566054 | 54 | 0.4492863 | 0.00 | pos | 3 | 2.825 | 3.175 | |
| SexFemale | -0.0015331 | 0.0027231 | 0.95 | -0.0068703 | 0.0038041 | -0.5629866 | 54 | 0.5734440 | -0.00 | neg | 2 | 1.825 | 2.175 | |
| RaceNon-White | 0.0031610 | 0.0031163 | 0.95 | -0.0029468 | 0.0092689 | 1.0143608 | 54 | 0.3104106 | 0.00 | pos | 1 | 0.825 | 1.175 |
model_growth_rate_cosmic_count_ddr <- glm(
formula = growth_rate ~ Trilaciclib + preVAF + CosmicCount + heme_cosmic_count + Cohort + Age + Sex + Race,
data = df_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(Trilaciclib != "Untreated") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(
term = case_when(
term == "TrilaciclibPlacebo" ~ "TrilaciclibVSPlacebo",
term == "CohortTNBC" ~ "SCLCvsTNBC",
term == "CohortCRC" ~ "SCLCvsCRC",
TRUE ~ term
)
)
model_growth_rate_cosmic_count_ddr| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TrilaciclibVSPlacebo | 0.0039312 | 0.0008866 | 0.95 | 0.0021935 | 0.0056690 | 4.4338790 | 350 | 0.0000093 | *** | 0.00 *** | pos | 9 | 8.825 | 9.175 |
| preVAF | -0.0369267 | 0.0167706 | 0.95 | -0.0697965 | -0.0040569 | -2.2018722 | 350 | 0.0276743 | * | -0.04 * | neg | 8 | 7.825 | 8.175 |
| CosmicCount | 0.0000000 | 0.0000043 | 0.95 | -0.0000085 | 0.0000085 | -0.0049319 | 350 | 0.9960650 | -0.00 | neg | 7 | 6.825 | 7.175 | |
| heme_cosmic_count | -0.0000521 | 0.0000826 | 0.95 | -0.0002140 | 0.0001097 | -0.6314511 | 350 | 0.5277456 | -0.00 | neg | 6 | 5.825 | 6.175 | |
| SCLCvsTNBC | -0.0025520 | 0.0014385 | 0.95 | -0.0053715 | 0.0002675 | -1.7740467 | 350 | 0.0760554 | -0.00 | neg | 5 | 4.825 | 5.175 | |
| SCLCvsCRC | -0.0046323 | 0.0013184 | 0.95 | -0.0072163 | -0.0020483 | -3.5136434 | 350 | 0.0004420 | *** | -0.00 *** | neg | 4 | 3.825 | 4.175 |
| Age | 0.0000476 | 0.0000408 | 0.95 | -0.0000323 | 0.0001276 | 1.1683493 | 350 | 0.2426659 | 0.00 | pos | 3 | 2.825 | 3.175 | |
| SexFemale | -0.0005090 | 0.0011997 | 0.95 | -0.0028605 | 0.0018424 | -0.4242895 | 350 | 0.6713547 | -0.00 | neg | 2 | 1.825 | 2.175 | |
| RaceNon-White | 0.0016333 | 0.0012415 | 0.95 | -0.0008001 | 0.0040667 | 1.3155584 | 350 | 0.1883223 | 0.00 | pos | 1 | 0.825 | 1.175 |
22 TP53 Line Plot
g1_tp %>%
filter(Gene_Class == "DDR", change_in_days < 1000) %>%
filter(preVAF > 0.01) %>%
filter(Cohort != "Untreated") %>%
filter(
ifelse(
Cohort == "TNBC",
duringVAF > 0.01,
postVAF > 0.01
)
) %>%
ggplot(
aes(
fill = Cohort,
color = Trilaciclib,
shape = Cohort
)
) +
geom_segment(
aes(
x = 0,
y = 0,
xend = change_in_days,
yend = change_in_VAF
),
size = 0.5
) +
geom_point(aes(x = change_in_days, y = change_in_VAF)) +
geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
labs(
x = "Change in Days",
y = "Change in VAF",
fill = "Cohort",
) +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.01),
limits = \(x) {
c(-max(abs(x)), max(abs(x)))
}
) +
theme(
plot.margin = margin(1, 1, 1, 1, "cm")
)
g1_tp %>%
filter(Gene == "TP53") %>%
filter(
ifelse(
Cohort == "TNBC",
duringVAF > 0.01,
postVAF > 0.01
)
) %>%
mutate(
pre = preVAF,
post = ifelse(Cohort == "TNBC", duringVAF, postVAF),
) %>%
dplyr::select(pre, post, change_in_days, Cohort, Trilaciclib) %>%
filter(change_in_days < 500) %>%
ggplot(aes(fill = Cohort, color = Cohort, shape = Trilaciclib)) +
geom_segment(
aes(
x = 0,
y = pre,
xend = change_in_days,
yend = post
),
size = 0.5
) +
geom_point(
aes(
x = change_in_days,
y = post
)
) +
geom_point(
aes(
x = 0,
y = pre
)
) +
labs(
x = "Change in Days",
y = "VAF",
fill = "Cohort",
) +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.01),
#limits = \(x) {
# c(-max(abs(x)), max(abs(x)))
#}
)
23 Age Ribbon Plots - TNBC and CRC
g1_mut_table %>%
filter(whichdraw == "PreTx") %>%
dplyr::select(DeID, Age, Trilaciclib, TP53, PPM1D, CHEK2) %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0)
) %>%
group_by(Trilaciclib) %>%
summarise(n_ddr_ch = sum(CH_Binary), n_total = n())| Trilaciclib | n_ddr_ch | n_total |
|---|---|---|
| Placebo | 12 | 37 |
| Trilaciclib | 5 | 28 |
| Untreated | 37 | 176 |
g1_mut_table_tnbc %>%
filter(whichdraw == "PreTx") %>%
dplyr::select(DeID, Age, Trilaciclib, TP53, PPM1D, CHEK2) %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0)
) %>%
group_by(Trilaciclib) %>%
summarise(n_ddr_ch = sum(CH_Binary), n_total = n())| Trilaciclib | n_ddr_ch | n_total |
|---|---|---|
| Placebo | 1 | 11 |
| Trilaciclib | 5 | 27 |
| Untreated | 37 | 176 |
g1_mut_table_crc %>%
filter(whichdraw == "PreTx") %>%
dplyr::select(DeID, Age, Trilaciclib, TP53, PPM1D, CHEK2) %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0)
) %>%
group_by(Trilaciclib) %>%
summarise(n_ddr_ch = sum(CH_Binary), n_total = n())| Trilaciclib | n_ddr_ch | n_total |
|---|---|---|
| Placebo | 22 | 112 |
| Trilaciclib | 20 | 100 |
| Untreated | 37 | 176 |
g1_mut_table_tnbc %>%
filter(whichdraw == "PreTx") %>%
dplyr::select(DeID, Age, Trilaciclib, TP53, PPM1D, CHEK2) %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0),
Treatment = ifelse(Trilaciclib == "Untreated", "Healthy Control", "Baseline TNBC"),
Treatment = factor(Treatment, levels = c("Healthy Control", "Baseline TNBC"))
) %>%
ggplot(
aes(
x = Age,
y = CH_Binary,
fill = Treatment
)
) +
geom_smooth(
aes(
fill = Treatment,
color = Treatment
),
method = "gam",
formula = y ~ s(x),
method.args = list(family = "binomial"),
size = 1.5,
se = TRUE,
alpha = 0.1
) +
scale_y_continuous(labels = scales::label_percent(accuracy = 1L), expand = expansion(add = c(0.01, 0.01))) +
scale_x_continuous(breaks = seq(0, 90, by = 5)) +
labs(x = "Age", y = "Frequency (%)") +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
#scale_fill_manual(values = treatment_colors) +
#scale_color_manual(values = treatment_colors) +
theme(
plot.margin = margin(0, 1, 1, 1, "cm")
)
g1_mut_table_crc %>%
filter(whichdraw == "PreTx", Age >= 45) %>%
mutate(
CH_Binary = ifelse(rowSums(across(where(is.integer)), na.rm = TRUE) > 0, 1, 0),
Treatment = ifelse(Trilaciclib == "Untreated", "Healthy Control", "Baseline CRC"),
Treatment = factor(Treatment, levels = c("Healthy Control", "Baseline CRC"))
) %>%
ggplot(
aes(
x = Age,
y = CH_Binary,
fill = Treatment
)
) +
geom_smooth(
aes(
fill = Treatment,
color = Treatment
),
method = "gam",
formula = y ~ s(x),
method.args = list(family = "binomial"),
size = 1.5,
se = TRUE,
alpha = 0.1
) +
scale_y_continuous(labels = scales::label_percent(accuracy = 1L), expand = expansion(add = c(0.01, 0.01))) +
scale_x_continuous(breaks = seq(0, 90, by = 5)) +
labs(x = "Age", y = "Frequency (%)") +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
#scale_fill_manual(values = treatment_colors) +
#scale_color_manual(values = treatment_colors) +
theme(
plot.margin = margin(0, 1, 1, 1, "cm")
)
24 Mutation Distribution Plot
g1_df %>% filter(Cohort == "TNBC" | Cohort == "Untreated") %>%
filter(whichdraw == "PreTx") %>%
mutate(
vaf_bin = case_when(
average_AF >= 0.002 & average_AF < 0.01 ~ "0.2-1% VAF",
average_AF >= 0.01 & average_AF < 0.05 ~ "1-5% VAF",
average_AF >= 0.05 ~ ">=5% VAF",
average_AF >= 0.001 & average_AF < 0.002 ~ "0.1-0.2% VAF"
)
) %>%
group_by(Gene, vaf_bin) %>%
summarise(n = n()) %>%
group_by(Gene) %>%
mutate(
total = sum(n),
percentage = (n / total) * 100,
#vaf_bin = factor(vaf_bin, levels = c("0.1-0.2% VAF", "0.2-1% VAF", "1-5% VAF", ">=5% VAF")),
vaf_bin = factor(vaf_bin, levels = c(">=5% VAF", "1-5% VAF", "0.2-1% VAF", "0.1-0.2% VAF")),
Gene = factor(Gene, levels = c(gene_list))
) %>%
ggplot(
aes(
x = Gene,
y = percentage,
fill = vaf_bin
)
) +
geom_bar(stat = "identity") +
labs(
x = "Gene",
y = "Percentage of Mutations at Baseline",
fill = "VAF Bin"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
#axis.text.y = element_text(angle = 90, hjust = 1)
plot.margin = margin(2, 1, 1, 1, "cm")
) +
scale_fill_nejm()## `summarise()` has grouped output by 'Gene'. You can override using the
## `.groups` argument.
g1_df %>% filter(Cohort == "CRC" | Cohort == "Untreated") %>%
filter(whichdraw == "PreTx") %>%
mutate(
vaf_bin = case_when(
average_AF >= 0.002 & average_AF < 0.01 ~ "0.2-1% VAF",
average_AF >= 0.01 & average_AF < 0.05 ~ "1-5% VAF",
average_AF >= 0.05 ~ ">=5% VAF",
average_AF >= 0.001 & average_AF < 0.002 ~ "0.1-0.2% VAF"
)
) %>%
group_by(Gene, vaf_bin) %>%
summarise(n = n()) %>%
group_by(Gene) %>%
mutate(
total = sum(n),
percentage = (n / total) * 100,
#vaf_bin = factor(vaf_bin, levels = c("0.1-0.2% VAF", "0.2-1% VAF", "1-5% VAF", ">=5% VAF")),
vaf_bin = factor(vaf_bin, levels = c(">=5% VAF", "1-5% VAF", "0.2-1% VAF", "0.1-0.2% VAF")),
Gene = factor(Gene, levels = c(gene_list))
) %>%
ggplot(
aes(
x = Gene,
y = percentage,
fill = vaf_bin
)
) +
geom_bar(stat = "identity") +
labs(
x = "Gene",
y = "Percentage of Mutations at Baseline",
fill = "VAF Bin"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
#axis.text.y = element_text(angle = 90, hjust = 1)
plot.margin = margin(2, 1, 1, 1, "cm")
) +
scale_fill_nejm()## `summarise()` has grouped output by 'Gene'. You can override using the
## `.groups` argument.
25 Mutation Proportion Plot
# TNBC
g1_mut_table_tnbc %>%
#filter(ifelse(Trilaciclib != "Untreated", whichdraw == "MidTx", whichdraw == "PostTx")) %>%
filter(whichdraw == "PreTx") %>%
mutate(Treatment = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))) %>%
group_by(Treatment) %>%
dplyr::select(-Age, -Trilaciclib, -whichdraw) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
pivot_longer(cols = -c(Treatment), names_to = "Gene", values_to = "n") %>%
group_by(Treatment, Gene) %>%
left_join(
g1_mut_table_tnbc %>%
#filter(ifelse(Trilaciclib != "Untreated", whichdraw == "MidTx", whichdraw == "PostTx")) %>%
filter(whichdraw == "PreTx") %>%
mutate(Treatment = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))) %>%
group_by(Treatment) %>%
summarise(total_patients = n())
) %>%
mutate(
Proportion = n / total_patients,
Gene = factor(Gene, levels = c(gene_list)),
Treatment = factor(Treatment, levels = c("Untreated", "Placebo", "Trilaciclib"))
#Trilaciclib = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))
) %>%
ggplot(
aes(
x = Gene,
y = Proportion,
fill = Treatment
)
) +
geom_bar(color = "black", stat = "identity", position = position_dodge()) +
labs(
x = "",
y = "Percent of Patients\nwith Mutated Gene\n Post-Treatment",
fill = "Treatment"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = c(0.8, 0.6),
legend.direction = "vertical",
plot.margin = margin(2, 0, 1, 1, "cm")
) +
scale_y_continuous(labels = scales::percent, limits = c(NA, 0.6), expand = c(0, 0)) +
scale_fill_nejm() +
scale_color_nejm()## Joining with `by = join_by(Treatment)`
# CRC
g1_mut_table_crc %>%
#filter(whichdraw == "PostTx") %>%
filter(whichdraw == "PreTx") %>%
mutate(Treatment = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))) %>%
group_by(Treatment) %>%
dplyr::select(-Age, -Trilaciclib, -whichdraw) %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
pivot_longer(cols = -c(Treatment), names_to = "Gene", values_to = "n") %>%
group_by(Treatment, Gene) %>%
left_join(
g1_mut_table_crc %>%
#filter(whichdraw == "PostTx") %>%
filter(whichdraw == "PreTx") %>%
mutate(Treatment = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))) %>%
group_by(Treatment) %>%
summarise(total_patients = n())
) %>%
mutate(
Proportion = n / total_patients,
Gene = factor(Gene, levels = c(gene_list)),
Treatment = factor(Treatment, levels = c("Untreated", "Placebo", "Trilaciclib"))
#Trilaciclib = factor(Trilaciclib, levels = c("Untreated", "Placebo", "Trilaciclib"))
) %>%
ggplot(
aes(
x = Gene,
y = Proportion,
fill = Treatment
)
) +
geom_bar(color = "black", stat = "identity", position = position_dodge()) +
labs(
x = "",
y = "Percent of Patients\nwith Mutated Gene\n Post-Treatment",
fill = "Treatment"
) +
panel_theme_basic +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = c(0.8, 0.6),
legend.direction = "vertical",
plot.margin = margin(2, 0, 1, 1, "cm")
) +
scale_y_continuous(labels = scales::percent, limits = c(NA, 0.6), expand = c(0, 0)) +
scale_fill_nejm() +
scale_color_nejm()## Joining with `by = join_by(Treatment)`
26 Blood Counts Analysis
g1_tp_tmp <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
filter(Cohort != "Untreated") %>%
mutate(Cohort = factor(Cohort, levels = c("SCLC", "TNBC", "CRC"))) %>%
mutate(
VAF_1 = preVAF * 100,
VAF_2 = case_when(
Cohort == "TNBC" ~ duringVAF * 100,
TRUE ~ postVAF * 100
),
log_VAF_1 = log(VAF_1),
log_VAF_2 = log(VAF_2),
) %>%
filter(
VAF_1 > 0 & VAF_2 > 0
)
model_change_in_prop_blood_counts <- map_dfr(c("DNMT3A", "TET2", "ASXL1", "TP53", "PPM1D", "CHEK2"), function(gene){
print(gene)
glm(
formula = VAF_2 ~ change_in_prop_BASO + change_in_prop_EOS + change_in_prop_LYM + change_in_prop_MONO + change_in_prop_NEUT + VAF_1,
family = "gaussian",
data = g1_tp_tmp %>% filter(Gene == gene)
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(Gene = gene)
})## [1] "DNMT3A"
## [1] "TET2"
## [1] "ASXL1"
## [1] "TP53"
## [1] "PPM1D"
## [1] "CHEK2"model_change_in_prop_blood_counts_2 <- map_dfr(c("DNMT3A", "TET2", "ASXL1", "TP53", "PPM1D", "CHEK2"), function(gene){
print(gene)
glm(
formula = VAF_2/VAF_1 ~ change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN + Cohort + Trilaciclib,
#formula = log_VAF_2 - log_VAF_1 ~ Trilaciclib * change_in_days,
#formula = growth_rate ~ Trilaciclib + change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN,
data = g1_tp_tmp %>% filter(Gene == gene) #%>% filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(Gene = gene)
})## [1] "DNMT3A"
## [1] "TET2"
## [1] "ASXL1"
## [1] "TP53"
## [1] "PPM1D"
## [1] "CHEK2"model_change_in_prop_blood_counts_2| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | Gene |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| change_in_prop_MONO | 1.4934571 | 9.2798258 | 0.95 | -16.6946672 | 19.681581 | 0.1609359 | 404 | 0.8721439 | 1.49 | pos | 6 | 5.825 | 6.175 | DNMT3A | |
| change_in_prop_LYM | 2.9195801 | 7.7817706 | 0.95 | -12.3324101 | 18.171570 | 0.3751820 | 404 | 0.7075251 | 2.92 | pos | 5 | 4.825 | 5.175 | DNMT3A | |
| change_in_prop_GRAN | 4.8243007 | 7.1597783 | 0.95 | -9.2086069 | 18.857208 | 0.6738059 | 404 | 0.5004347 | 4.82 | pos | 4 | 3.825 | 4.175 | DNMT3A | |
| CohortTNBC | 0.2303400 | 1.3021358 | 0.95 | -2.3217993 | 2.782479 | 0.1768940 | 404 | 0.8595917 | 0.23 | pos | 3 | 2.825 | 3.175 | DNMT3A | |
| CohortCRC | 1.2950453 | 0.9976450 | 0.95 | -0.6603029 | 3.250393 | 1.2981024 | 404 | 0.1942522 | 1.30 | pos | 2 | 1.825 | 2.175 | DNMT3A | |
| TrilaciclibPlacebo | 0.0226533 | 0.7063164 | 0.95 | -1.3617014 | 1.407008 | 0.0320724 | 404 | 0.9744143 | 0.02 | pos | 1 | 0.825 | 1.175 | DNMT3A | |
| change_in_prop_MONO | 10.8698045 | 38.9406258 | 0.95 | -65.4524197 | 87.192029 | 0.2791379 | 147 | 0.7801390 | 10.87 | pos | 6 | 5.825 | 6.175 | TET2 | |
| change_in_prop_LYM | -18.6877544 | 37.4494126 | 0.95 | -92.0872543 | 54.711745 | -0.4990133 | 147 | 0.6177700 | -18.69 | neg | 5 | 4.825 | 5.175 | TET2 | |
| change_in_prop_GRAN | -5.0619467 | 36.1955257 | 0.95 | -76.0038734 | 65.879980 | -0.1398501 | 147 | 0.8887785 | -5.06 | neg | 4 | 3.825 | 4.175 | TET2 | |
| CohortTNBC | -0.2903057 | 5.8173777 | 0.95 | -11.6921565 | 11.111545 | -0.0499032 | 147 | 0.9601995 | -0.29 | neg | 3 | 2.825 | 3.175 | TET2 | |
| CohortCRC | 4.5688181 | 4.8919581 | 0.95 | -5.0192435 | 14.156880 | 0.9339447 | 147 | 0.3503324 | 4.57 | pos | 2 | 1.825 | 2.175 | TET2 | |
| TrilaciclibPlacebo | 0.9566802 | 3.0645785 | 0.95 | -5.0497833 | 6.963144 | 0.3121735 | 147 | 0.7549087 | 0.96 | pos | 1 | 0.825 | 1.175 | TET2 | |
| change_in_prop_MONO | 32.6829535 | 67.8662777 | 0.95 | -100.3325065 | 165.698414 | 0.4815787 | 33 | 0.6301053 | 32.68 | pos | 6 | 5.825 | 6.175 | ASXL1 | |
| change_in_prop_LYM | -49.9341114 | 91.0194476 | 0.95 | -228.3289505 | 128.460728 | -0.5486093 | 33 | 0.5832736 | -49.93 | neg | 5 | 4.825 | 5.175 | ASXL1 | |
| change_in_prop_GRAN | -0.7639099 | 93.9469410 | 0.95 | -184.8965306 | 183.368711 | -0.0081313 | 33 | 0.9935122 | -0.76 | neg | 4 | 3.825 | 4.175 | ASXL1 | |
| CohortTNBC | -4.8439631 | 22.8901059 | 0.95 | -49.7077463 | 40.019820 | -0.2116182 | 33 | 0.8324049 | -4.84 | neg | 3 | 2.825 | 3.175 | ASXL1 | |
| CohortCRC | 10.1420710 | 19.4622936 | 0.95 | -28.0033236 | 48.287466 | 0.5211139 | 33 | 0.6022875 | 10.14 | pos | 2 | 1.825 | 2.175 | ASXL1 | |
| TrilaciclibPlacebo | 5.4257347 | 10.8191084 | 0.95 | -15.7793280 | 26.630797 | 0.5014955 | 33 | 0.6160224 | 5.43 | pos | 1 | 0.825 | 1.175 | ASXL1 | |
| change_in_prop_MONO | -4.4459468 | 19.4346827 | 0.95 | -42.5372250 | 33.645331 | -0.2287635 | 118 | 0.8190527 | -4.45 | neg | 6 | 5.825 | 6.175 | TP53 | |
| change_in_prop_LYM | -14.3244238 | 17.8983893 | 0.95 | -49.4046221 | 20.755775 | -0.8003192 | 118 | 0.4235259 | -14.32 | neg | 5 | 4.825 | 5.175 | TP53 | |
| change_in_prop_GRAN | -7.3212179 | 16.8697247 | 0.95 | -40.3852707 | 25.742835 | -0.4339856 | 118 | 0.6642989 | -7.32 | neg | 4 | 3.825 | 4.175 | TP53 | |
| CohortTNBC | 1.2022635 | 2.0104765 | 0.95 | -2.7381981 | 5.142725 | 0.5979993 | 118 | 0.5498404 | 1.20 | pos | 3 | 2.825 | 3.175 | TP53 | |
| CohortCRC | 1.9215089 | 1.7619594 | 0.95 | -1.5318680 | 5.374886 | 1.0905523 | 118 | 0.2754699 | 1.92 | pos | 2 | 1.825 | 2.175 | TP53 | |
| TrilaciclibPlacebo | 2.0939412 | 1.2192743 | 0.95 | -0.2957925 | 4.483675 | 1.7173668 | 118 | 0.0859122 | 2.09 | pos | 1 | 0.825 | 1.175 | TP53 | |
| change_in_prop_MONO | 6.4896568 | 25.4437413 | 0.95 | -43.3791599 | 56.358473 | 0.2550591 | 151 | 0.7986775 | 6.49 | pos | 6 | 5.825 | 6.175 | PPM1D | |
| change_in_prop_LYM | -15.8941530 | 31.7932360 | 0.95 | -78.2077504 | 46.419444 | -0.4999225 | 151 | 0.6171297 | -15.89 | neg | 5 | 4.825 | 5.175 | PPM1D | |
| change_in_prop_GRAN | -13.2883943 | 29.4235846 | 0.95 | -70.9575605 | 44.380772 | -0.4516239 | 151 | 0.6515399 | -13.29 | neg | 4 | 3.825 | 4.175 | PPM1D | |
| CohortTNBC | 4.2047565 | 4.7888556 | 0.95 | -5.1812280 | 13.590741 | 0.8780295 | 151 | 0.3799277 | 4.20 | pos | 3 | 2.825 | 3.175 | PPM1D | |
| CohortCRC | 3.4026267 | 3.6820805 | 0.95 | -3.8141185 | 10.619372 | 0.9241044 | 151 | 0.3554320 | 3.40 | pos | 2 | 1.825 | 2.175 | PPM1D | |
| TrilaciclibPlacebo | 8.0349910 | 2.7312467 | 0.95 | 2.6818459 | 13.388136 | 2.9418767 | 151 | 0.0032623 | ** | 8.03 ** | pos | 1 | 0.825 | 1.175 | PPM1D |
| change_in_prop_MONO | 20.4603604 | 52.8303756 | 0.95 | -83.0852730 | 124.005994 | 0.3872840 | 53 | 0.6985460 | 20.46 | pos | 6 | 5.825 | 6.175 | CHEK2 | |
| change_in_prop_LYM | -41.9759607 | 41.9903306 | 0.95 | -124.2754965 | 40.323575 | -0.9996578 | 53 | 0.3174762 | -41.98 | neg | 5 | 4.825 | 5.175 | CHEK2 | |
| change_in_prop_GRAN | -19.3816157 | 37.3783051 | 0.95 | -92.6417476 | 53.878516 | -0.5185258 | 53 | 0.6040915 | -19.38 | neg | 4 | 3.825 | 4.175 | CHEK2 | |
| CohortTNBC | -1.9989926 | 5.5032248 | 0.95 | -12.7851149 | 8.787130 | -0.3632402 | 53 | 0.7164254 | -2.00 | neg | 3 | 2.825 | 3.175 | CHEK2 | |
| CohortCRC | 3.2217396 | 4.1433286 | 0.95 | -4.8990352 | 11.342514 | 0.7775728 | 53 | 0.4368209 | 3.22 | pos | 2 | 1.825 | 2.175 | CHEK2 | |
| TrilaciclibPlacebo | 3.6986406 | 3.2078433 | 0.95 | -2.5886167 | 9.985898 | 1.1529992 | 53 | 0.2489107 | 3.70 | pos | 1 | 0.825 | 1.175 | CHEK2 |
model_change_in_prop_blood_counts_3 <- map_dfr(c("DTA", "DDR"), function(gene_class){
print(gene_class)
glm(
formula = VAF_2/VAF_1 ~ change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN + Cohort + Trilaciclib,
#formula = log_VAF_2 - log_VAF_1 ~ Trilaciclib * change_in_days,
#formula = growth_rate ~ Trilaciclib + change_in_prop_MONO + change_in_prop_LYM + change_in_prop_GRAN,
data = g1_tp_tmp %>% filter(Gene_Class == gene_class) #%>% filter(growth_rate != "Inf" & growth_rate != "-Inf")
) %>%
sjPlot::get_model_data(type = "est") %>%
mutate(Gene_Class = gene_class)
})## [1] "DTA"
## [1] "DDR"model_change_in_prop_blood_counts_3| term | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value | p.stars | p.label | group | xpos | xmin | xmax | Gene_Class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| change_in_prop_MONO | 0.9952329 | 12.769689 | 0.95 | -24.0328969 | 26.0233627 | 0.0779371 | 598 | 0.9378781 | 1.00 | pos | 6 | 5.825 | 6.175 | DTA | |
| change_in_prop_LYM | -6.3158440 | 11.855610 | 0.95 | -29.5524130 | 16.9207250 | -0.5327304 | 598 | 0.5942202 | -6.32 | neg | 5 | 4.825 | 5.175 | DTA | |
| change_in_prop_GRAN | 1.8785401 | 11.086021 | 0.95 | -19.8496610 | 23.6067411 | 0.1694513 | 598 | 0.8654417 | 1.88 | pos | 4 | 3.825 | 4.175 | DTA | |
| CohortTNBC | 0.4829915 | 1.993335 | 0.95 | -3.4238739 | 4.3898569 | 0.2423032 | 598 | 0.8085452 | 0.48 | pos | 3 | 2.825 | 3.175 | DTA | |
| CohortCRC | 3.1266624 | 1.580680 | 0.95 | 0.0285868 | 6.2247381 | 1.9780491 | 598 | 0.0479232 | * | 3.13 * | pos | 2 | 1.825 | 2.175 | DTA |
| TrilaciclibPlacebo | 0.9965148 | 1.070084 | 0.95 | -1.1008114 | 3.0938410 | 0.9312491 | 598 | 0.3517247 | 1.00 | pos | 1 | 0.825 | 1.175 | DTA | |
| change_in_prop_MONO | -6.7311422 | 16.612319 | 0.95 | -39.2906882 | 25.8284038 | -0.4051898 | 336 | 0.6853380 | -6.73 | neg | 6 | 5.825 | 6.175 | DDR | |
| change_in_prop_LYM | -34.6267325 | 17.752422 | 0.95 | -69.4208401 | 0.1673752 | -1.9505357 | 336 | 0.0511123 | -34.63 | neg | 5 | 4.825 | 5.175 | DDR | |
| change_in_prop_GRAN | -23.7147043 | 16.479162 | 0.95 | -56.0132679 | 8.5838593 | -1.4390722 | 336 | 0.1501301 | -23.71 | neg | 4 | 3.825 | 4.175 | DDR | |
| CohortTNBC | 1.0635118 | 2.477417 | 0.95 | -3.7921366 | 5.9191602 | 0.4292825 | 336 | 0.6677177 | 1.06 | pos | 3 | 2.825 | 3.175 | DDR | |
| CohortCRC | 2.7879841 | 2.028004 | 0.95 | -1.1868306 | 6.7627987 | 1.3747429 | 336 | 0.1692112 | 2.79 | pos | 2 | 1.825 | 2.175 | DDR | |
| TrilaciclibPlacebo | 4.7567901 | 1.471579 | 0.95 | 1.8725477 | 7.6410325 | 3.2324389 | 336 | 0.0012274 | ** | 4.76 ** | pos | 1 | 0.825 | 1.175 | DDR |
27 For Reviewer Comments
# Spider Plot with Average Mean Slope
g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
filter(!is.na(change_in_VAF), !is.na(Gene_Class)) %>%
mutate(
slope = change_in_VAF / change_in_days,
starting_vaf = ifelse(preVAF < 0.02, "<0.02 Starting VAF", ">=0.02 Starting VAF")
) %>%
group_by(Trilaciclib, Gene_Class, starting_vaf) %>%
summarise(
mean_slope = mean(slope),
se_slope = sd(slope) / sqrt(n()),
.groups = "drop"
) %>%
group_by(Trilaciclib, Gene_Class, starting_vaf) %>%
do({
x_seq <- seq(0, max(g1_tp$change_in_days), length.out = 100)
data.frame(
x = x_seq,
y = .$mean_slope * x_seq,
ymin = (.$mean_slope - .$se_slope) * x_seq,
ymax = (.$mean_slope + .$se_slope) * x_seq,
Trilaciclib = .$Trilaciclib
)
}) %>%
ggplot(
aes(x = x, color = Trilaciclib, fill = Trilaciclib)
) +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.2) +
geom_line(aes(y = y), size = 1, linetype = "dashed") +
geom_point(
data = g1_tp %>%
filter(!is.na(change_in_VAF), !is.na(Gene_Class)),
aes(x = change_in_days, y = change_in_VAF),
alpha = 0.5
) +
geom_segment(
data = g1_tp %>%
filter(!is.na(change_in_VAF), !is.na(Gene_Class)),
aes(
x = 0,
y = 0,
xend = change_in_days,
yend = change_in_VAF
),
alpha = 0.1
) +
scale_color_nejm() +
scale_fill_nejm() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.001),
limits = \(x) {
c(-max(abs(x)), max(abs(x)))
}
) +
labs(x = "Change in Days", y = "Change in VAF") +
facet_wrap(~Gene_Class+starting_vaf, nrow = 2, ncol = 2) +
panel_theme_basic +
theme(
plot.margin = margin(1, 1, 1, 1, "cm")
)
# Generic Spider Plot
g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
filter(!is.na(change_in_VAF), Gene_Class == "DDR" | Gene_Class == "DTA") %>%
ggplot(
aes(
x = change_in_days,
y = change_in_VAF,
fill = Trilaciclib
)
) +
geom_segment(
aes(
x = change_in_days,
y = change_in_VAF,
xend = 0,
yend = 0,
color = Trilaciclib
),
size = 0.1,
alpha = 0.5
) +
geom_point(
aes(
x = change_in_days,
y = change_in_VAF,
color = Trilaciclib
),
alpha = 0.5
) +
labs(
x = "",
y = "Change in VAF",
fill = "Trilaciclib",
) +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.001),
limits = \(x) {
c(-max(abs(x)), max(abs(x)))
}
) +
scale_x_sqrt() +
facet_wrap(~Gene_Class) +
theme(
strip.text = element_blank(),
plot.title = element_text(hjust = 0, vjust = 2, size = 20),
plot.margin = margin(1, 1, 1, 1, "cm"),
panel.spacing = unit(2, "lines")
)
# Spider Plot by Gene with Starting VAF
g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>%
ungroup() %>%
group_by(DeID, Gene) %>%
mutate(
change_in_days = mean(change_in_days),
change_in_VAF = mean(change_in_VAF),
preVAF = mean(preVAF)
) %>%
ungroup() %>%
ggplot(
aes(
x = change_in_days,
y = change_in_VAF,
color = Trilaciclib,
)
) +
geom_point(
aes(x = change_in_days, y = change_in_VAF, size = preVAF),
alpha = 0.5
) +
geom_segment(
aes(
x = 0,
y = 0,
xend = change_in_days,
yend = change_in_VAF
),
alpha = 0.1
) +
labs(
x = "Change in Days",
y = "Change in VAF",
color = "Trilaciclib"
) +
panel_theme_basic +
scale_fill_nejm() +
scale_color_nejm() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.001),
limits = \(x) {
c(-max(abs(x)), max(abs(x)))
},
labels = scales::percent_format(accuracy = 0.1)
) +
theme(
plot.margin = margin(1, 1, 1, 1, "cm"),
# legend.position = "bottom"
) +
facet_wrap(~Gene)
# Taking the Largest Clone per Patient per Gene
data_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>%
group_by(DeID, Gene) %>%
slice_max(abs(change_in_VAF), n = 1) %>%
ungroup() %>%
mutate(
# Setting Alpha Value to indicate how much the VAF has changed
alpha_val = case_when(
abs(growth_rate) < 0.01 ~ 0.2,
abs(growth_rate) >= 0.01 & abs(growth_rate) < 0.05 ~ 0.6,
abs(growth_rate) >= 0.5 ~ 0.9
),
# Defining the size of the point based on the starting VAF
starting_vaf = case_when(
preVAF < 0.02 ~ "<2%",
preVAF >= 0.02 & preVAF < 0.05 ~ "2-5%",
preVAF >= 0.05 ~ ">=5%"
),
starting_vaf = factor(starting_vaf, levels = c("<2%", "2-5%", ">=5%"))
)
data_to_plot %>%
mutate(
slope = change_in_VAF / change_in_days
) %>%
group_by(Trilaciclib, Gene_Class) %>%
summarise(
mean_slope = mean(slope),
se_slope = sd(slope) / sqrt(n()),
.groups = "drop"
) %>%
group_by(Trilaciclib, Gene_Class) %>%
do({
x_seq <- seq(0, max(g1_tp$change_in_days), length.out = 100)
data.frame(
x = x_seq,
y = .$mean_slope * x_seq,
ymin = (.$mean_slope - .$se_slope) * x_seq,
ymax = (.$mean_slope + .$se_slope) * x_seq,
Trilaciclib = .$Trilaciclib
)
}) %>%
# ggplot(
# aes(
# x = change_in_days,
# y = change_in_VAF,
# color = Trilaciclib,
# size = starting_vaf,
# alpha = alpha_val
# )
# ) +
ggplot(
aes(x = x, color = Trilaciclib, fill = Trilaciclib)
) +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.2) +
geom_line(aes(y = y), size = 1, linetype = "dashed") +
geom_point(
data = data_to_plot,
aes(x = change_in_days, y = change_in_VAF, size = starting_vaf, alpha = alpha_val),
) +
geom_segment(
data = data_to_plot,
aes(
x = 0,
y = 0,
xend = change_in_days,
yend = change_in_VAF,
#alpha = alpha_val
),
alpha = 0.1,
size = 0.5,
) +
scale_alpha_identity() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.001),
limits = \(x) {
c(-max(abs(x)), max(abs(x)))
},
labels = scales::percent_format(accuracy = 0.1)
) +
#scale_x_log10() +
labs(
x = "Change in Days",
y = "Change in VAF",
color = "Trilaciclib",
size = "PreVAF"
) +
panel_theme_basic +
scale_fill_manual(values = treatment_colors) +
scale_color_manual(values = treatment_colors) +
facet_wrap(~Gene_Class + Trilaciclib) +
theme(
axis.text.y = element_text(size = 16),
)## Warning: Using size for a discrete variable is not advised.
# The idea is that depending on the starting VAF, the change in VAF will be different. This is a way to show that the change in VAF is dependent on the starting VAF.
data_to_plot <- g1_tp %>%
filter(
case_when(
Cohort == "Untreated" ~ compare_group == "PrevsPost",
Cohort == "SCLC" ~ compare_group == "C1D1vsC5D1",
Cohort == "CRC" ~ compare_group == "C1D1vsMaintenance",
Cohort == "TNBC" ~ compare_group == "C1D1vsC7D1",
TRUE ~ TRUE
)
) %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
#filter(Gene_Class == "DDR" | Gene_Class == "DTA") %>%
filter(Gene_Class == "DDR") %>%
group_by(DeID, Gene) %>%
slice_max(abs(change_in_VAF), n = 1) %>%
ungroup() %>%
mutate(
starting_clone_vaf = case_when(
preVAF < 0.02 ~ "<2%",
preVAF >= 0.02 & preVAF < 0.05 ~ "2-5%",
preVAF >= 0.05 ~ ">=5%"
),
starting_clone_vaf = factor(starting_clone_vaf, levels = c("<2%", "2-5%", ">=5%")),
treatment_time = case_when(
change_in_days < 30 ~ "1 Month",
change_in_days >= 30 & change_in_days < 90 ~ "3 Months",
change_in_days >= 90 & change_in_days < 180 ~ "Half a Year",
change_in_days >= 180 & change_in_days < 360 ~ "A Year",
change_in_days >= 360 ~ "Over A Year"
),
treatment_time = factor(treatment_time, levels = c("1 Month", "3 Months", "Half a Year", "A Year")),
alpha_val = case_when(
abs(growth_rate) < 0.01 ~ 0.3,
abs(growth_rate) >= 0.01 & abs(growth_rate) < 0.05 ~ 0.5,
abs(growth_rate) >= 0.05 ~ 1
)
) %>%
mutate(
change_in_months = change_in_days / 30,
change_in_VAF_per_month = change_in_VAF / change_in_months,
change_in_VAF_per_day = change_in_VAF / change_in_days,
)
# Statistics
model_change_in_VAF <- rbind(
glm(
formula = change_in_VAF_per_month ~ Trilaciclib + Age,
data = data_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Cohort == "SCLC")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(Cohort = "SCLC"),
glm(
formula = change_in_VAF_per_month ~ Trilaciclib + Age,
data = data_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Cohort == "TNBC")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(Cohort = "TNBC"),
glm(
formula = change_in_VAF_per_month ~ Trilaciclib + Age,
data = data_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Cohort == "CRC")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(Cohort = "CRC")
)
model_change_in_VAF <- model_change_in_VAF %>% filter(term == "TrilaciclibPlacebo")
model_change_in_VAF <- rbind(
glm(
formula = change_in_VAF ~ Trilaciclib + Age,
data = data_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Cohort == "SCLC")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(Cohort = "SCLC"),
glm(
formula = change_in_VAF ~ Trilaciclib + Age,
data = data_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Cohort == "TNBC")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(Cohort = "TNBC"),
glm(
formula = change_in_VAF ~ Trilaciclib + Age,
data = data_to_plot %>%
filter(Gene_Class == "DDR") %>%
filter(growth_rate != "Inf" & growth_rate != "-Inf") %>%
filter(Cohort == "CRC")
) %>% sjPlot::get_model_data(type = "est") %>%
mutate(Cohort = "CRC")
)
model_change_in_VAF <- model_change_in_VAF %>% filter(term == "TrilaciclibPlacebo")
data_to_plot %>%
ggplot(
aes(
x = Cohort,
y = change_in_VAF,
color = Trilaciclib,
size = starting_clone_vaf,
alpha = alpha_val
)
) +
geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.1, size = 1, outlier.shape = NA) +
geom_point(position = position_jitterdodge(jitter.width=1)) +
#facet_wrap(~Gene_Class) +
#geom_point(position = position_dodge(width = 0.8), alpha = 0.3) +
labs(
x = "",
y = "Change in VAF",
color = "Trilaciclib",
size = "Starting Clone VAF",
) +
panel_theme_basic +
#scale_fill_nejm() +
#scale_color_nejm() +
scale_fill_manual(values = treatment_colors) +
scale_color_manual(values = treatment_colors) +
scale_alpha_identity() +
scale_y_continuous(
trans = scales::pseudo_log_trans(0.0001),
limits = \(x) {c(-max(abs(x)), max(abs(x)))},
#breaks = scales::extended_breaks(n = 10),
breaks = c(-0.1, -0.05, -0.01, -0.005, -0.001, 0, 0.001, 0.005, 0.01, 0.05, 0.1),
labels = scales::percent_format(accuracy = 0.1)
) +
theme(
legend.position = "right",
legend.direction = "vertical",
legend.title = element_text(size = 18),
legend.text = element_text(size = 18)
) +
guides(
color = guide_legend(title.position = "top", title = "Drug Treatment"),
size = guide_legend(title.position = "top")
) +
do.call(geom_signif, default_sig_data(data_to_plot %>% filter(Cohort == "SCLC"), 6, 1.8, 2.2, model_change_in_VAF$p.value[1], model_change_in_VAF$p.stars[1], show_values = TRUE)) +
do.call(geom_signif, default_sig_data(data_to_plot %>% filter(Cohort == "TNBC"), 7, 2.8, 3.2, model_change_in_VAF$p.value[2], model_change_in_VAF$p.stars[2], show_values = TRUE)) +
do.call(geom_signif, default_sig_data(data_to_plot %>% filter(Cohort == "CRC"), 8, 3.8, 4.2, model_change_in_VAF$p.value[3], model_change_in_VAF$p.stars[3], show_values = TRUE))## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning in (function (mapping = NULL, data = NULL, stat = "signif", position =
## "identity", : Ignoring unknown parameters: `fill`## Warning: Using size for a discrete variable is not advised.## Warning: The following aesthetics were dropped during statistical transformation: alpha.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: alpha.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: alpha.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's fill values.
# Density plot of preVAF
g1_tp %>%
ggplot(
aes(
x = growth_rate,
fill = Trilaciclib
)
) +
geom_density(alpha = 0.5) +
scale_x_log10() +
labs(
x = "PreVAF",
fill = "Trilaciclib"
) +
panel_theme_basic +
scale_fill_nejm() +
theme(
plot.margin = margin(1, 1, 1, 1, "cm")
)## Warning in transformation$transform(x): NaNs produced## Warning in scale_x_log10(): log-10 transformation introduced infinite values.## Warning: Removed 703 rows containing non-finite outside the scale range
## (`stat_density()`).
supp_table_3 <- g1_tp %>%
dplyr::select(DeID, Cohort, Trilaciclib, Gene, key, prepreVAF, preVAF, duringVAF, postVAF, postpostVAF, compare_group, change_in_days) %>%
mutate(
Timepoint_1_VAF = preVAF,
Timepoint_2_VAF = case_when(
compare_group == "PrevsPost" ~ postVAF,
compare_group == "C1D1vsC5D1" ~ postVAF,
compare_group == "C1D1vs90DPost-Maintenance" ~ postpostVAF,
compare_group == "C1D1vsC7D1" ~ duringVAF,
compare_group == "C1D1vsMaintenance" ~ postVAF
),
change_in_days = round(change_in_days)
) %>%
dplyr::select(-c(prepreVAF, preVAF, duringVAF, postVAF, postpostVAF, compare_group))
#fwrite(supp_table_3, "supp_table_3.csv")